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Booker, Alan Stephen.
Modeling the 100-year flood using GIS :
b a flood analysis in the Avon Park watershed
h [electronic resource] /
by Alan Stephen Booker.
[Tampa, Fla] :
University of South Florida,
ABSTRACT: Using hydraulic modeling and Geographic Information System (GIS) software, the 100-year flood was delineated for the municipality of Avon Park located in Highlands County, Florida. A detailed and rigorous approach was undertaken to first collect and develop an extensive spatial database to store the data collected that is pertinent to the model. This analysis combined ArcGIS version 8.3 and the Interconnected Channel and Pond Routing (ICPR) model version 3.02 to develop hydraulic models that assigned regulatory flood elevation within the watershed. The model results were post processed and brought into GIS to delineate the 100-year flood.The steps outlined in this thesis with respect to the use of GIS as a tool in model pre and post-processing are applicable to many models. Hence, the methodology outlined in this thesis adds to the existing pool of knowledge about the use of GIS in hydraulic modeling. By documenting all the steps related to data acquisition, data processing and manipulation, the model interface and GIS, and the post processing of the model results, this thesis can serve as resources for future studies that utilize both GIS and hydraulic modeling.
Thesis (M.A.)--University of South Florida, 2006.
Includes bibliographical references.
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Adviser: Philip Reeder, Ph.D.
t USF Electronic Theses and Dissertations.
Modeling the 100-Year Flood Using GIS: A Flood Analysis in th e Avon Park Watershed by Alan Stephen Booker A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts Department of Geography College of Arts and Sciences University of South Florida Major Professor: Philip Reeder, Ph.D. Mark Ross, Ph D. Kenneth Trout, Ph.D. Paul Zandbergen, Ph.D. Date of Approval: April 14, 2006 Keywords: Hydrology, Geography, I nundation, Hurricanes, Delineation Copyright 2006, Alan S. Booker
i Table of Contents List of Tables iii List of Figures iv Abstract vi Chapter One. Introduction 1 Background 1 Goal 2 Rational and Justification 3 Chapter Two. Literature Review 5 Hydrologic Theories 5 Consequences of Flooding 8 100-Year Flood Definition and Implications 11 Hydraulic Models 13 Geographic Information Systems 23 Chapter Three. Study Area 30 Avon Park Watershed 30 Topography 33 Water bodies 35 Soils 35
ii Landuse 38 Chapter Four. Methodology 40 Data Sources 40 Watershed Evaluation and Parameterization 44 Data Input into the model 77 Chapter Five. Results and Discussion 78 Model Results 78 Delineation 85 Reviewing the floodplain delineation procedure 96 Chapter Six. Conclusion 103 Summary and General Procedure 103 Method Suitability 104 Future Studies 106 Literature Cited 110
iii List of Tables Table 1: Summary Statistics of the Lakes 35 Table 2: Summary Statistics of the Soil Types 36 Table 3: Summary Statistics of the Hydrologic Soil groups 36 Table 4: Summary Statistics of the Landuse types 38 Table 5: Data Acquisition 41 Table 6: Data Creati on and pre-processing 42 Table 7: Highlands County Rainfall Frequency Amount 51 Table 8: Watershed Parameter Assignments Curve Number 65 Table 9: Watershed Parameter Assign ments Mannings n and Percent DCIA 66 Table 10: Watershed Parameter Assignments 67 Table 11: Model Verification Using Hurricane Jeanne Event 77 Table 12: 100-Year 24 Hour and 5 Day Model Results 80 Table 13: 100-Year Max Stage 83
iv List of Figures Figure 1 Location Map 32 Figure 2 Topography 34 Figure 4 Landuse Map 39 Figure 5 Methodology 40 Figure 6 Study Area 43 Figure 7 1989 One-Foot Contours 46 Figure 8 Digital Terrai n Model Development 47 Figure 9: Rainfall Stations 50 Figure 10: Hurricane Jeanne Rainfall Distribution 51 Figure 11 Avon Park Subbasins 53 Figure 12 Lake Verona 54 Figure 13 Dry Channel 55 Figure 14 Airport Ditch 55 Figure 15 Hydraulic Inventory 57 Figure 16 Hydraulic Network 59 Figure 17 Time of Concentration 60 Figure 18 Flow Lines 62 Figure 19 Flow Lines Converted to Points 63 Figure 20 Cross Section Profile 69
v Figure 21 Cross Sections 71 Figure 22 Lake Tulane Subbasins 73 Figure 23 Lake Tulane Storage 74 Figure 24 Lake Tulane Storage TIN 76 Figure 25 Floodplain Delineation 79 Figure 26 Mapping Cross sect ions and Mapping Polygons 87 Figure 27 Maximum Water Surface Elevation TIN 90 Figure 28 Flooded and Non Flooded Areas 91 Figure 29 100-Year Flood 92 Figure 30 100-Year Flood Around the Airport 93 Figure 31 100-Year Flood Around Lake Anoka and Lake Tulane 94 Figure 32 100-Year Flood Around Lake Isis and Lake Verona 95 Figure 33 1996 FEMA Flood Zones 98 Figure 34 Simulated 100-Year & 1996 FEMA Flood Zones 99 Figure 35 Flood Depth Associated With The 100-Year Flood 101
vi Modeling the 100-Year Flood Using GIS: A Flood Analysis in th e Avon Park Watershed Alan S. Booker ABSTRACT Using hydraulic modeling and Geographic Information System (GIS) software, the 100-year flood was delineated for the munici pality of Avon Park located in Highlands County, Florida. A detailed and rigorous a pproach was undertaken to first collect and develop an extensive spatial data base to store the data collected that is pertinent to the model. This analysis combined ArcGIS ve rsion 8.3 and the Interconnected Channel and Pond Routing (ICPR) model version 3.02 to develop hydraulic models that assigned regulatory flood elevation with in the watershed. The model results were post processed and brought into GIS to delin eate the 100-year flood. The steps outlined in this thesis with resp ect to the use of GIS as a tool in model pre and post-processing are applicable to many models. Hence, the methodology outlined in this thesis adds to the existing pool of knowledge about the use of GIS in hydraulic modeling. By documenting all the steps related to data acquisition, data processing and manipulation, the model interface and GIS, and the post processing of the model results, this thesis can serve as resour ces for future studies that utilize both GIS and hydraulic modeling.
1 Chapter One Introduction Background Devastating flood inundations occur thr ough out the United States every year impacting homes, businesses, and public infrastructures. With very few exceptions, almost all areas of the United States are subj ect to some kind of flooding when the right set of climatic conditions occur. Despite exte nsive research to determine areas that are prone to flooding, there is still much research that needs to be completed. Flood damage can be disastrous in terms of material goods, loss of business productivity, social disruption, and more importantl y, human fatalities. Thus, flood study is essential as this knowledge can facilitate so ciety to better prepare and mitigate flood events. New housing developments have propagated at such a fast pace in states like Florida that new studies need to be completed or revise d frequently. For the purpose of National Flood Insurance Program, the Federal Emergency Management Agency (FEMA) has a responsibility to identify and update flood hazard information (FEMA 2005b). Despite the efforts of the government it appears that the bulk of flood studies has been conducted on larger urban areas, wh ile smaller towns have been somewhat neglected. The city of Avon Park, which is located in Highlands C ounty, Florida, is one of those areas that had not been extensivel y studied in terms of inundations by FEMA or
2 other agencies. In fact, the most densely populated portion of the municipality of Avon Park has yet to be fully mapped by FEMA. This thesis will describe the methodology th at is essential to model the 100-year flood, using the Avon Park watershed as a case study site (Figure 1). The overall contribution that this research will make to the existing pool of knowledge is to demonstrate how Geographic Information Systems (GIS) can be utilized to generate and store the data for a hydraulic model, and how GI S can also be used to spatially represent the 100-year flood based on the model results. By documenting all the necessary steps related to data acquisition, data processing and manipulation, the model interface and GIS, and post processing of the model result s, this thesis can serve as a resource for future studies that utilize both GIS and hydraulic modeling software. Goal This study will draw upon a standard m odeling methodology and advancement of GIS to determine areas that would be inunda ted in the 100-year fl ood event in the Avon Park watershed. This research will not focus on all the detailed aspects of the model, but instead will focus on how GIS can play an in tegral role in modeling the flood and the watershed. By first exploring how GIS can be utilized in creati ng a flood model for the Avon Park watershed using the Interconnected Channel and Pond Routing model (ICPR) version 3.02, and then documenting all the nece ssary steps to prepare, run, and analyze the output of the model, this thesis will provid e a framework for future studies. ICPR is a hydraulic model that meets FEMA requirement s for assigning regul atory flood elevation, but it requires extensive data acquisition and pr e-processing to run the model. GIS can be
3 used to delineate the 100-year flood generated from the mode l results. In the past, the 100-year flood stage was manually digitized, wh ich was a time consuming process. This requirement was not very time or cost eff ective because different model scenarios or different return periods may be needed to be spatially represented. A methodology developed by Watershed Concepts (2004) was introduced to limit the amount of manual work involved in floodplain delineation from models that have no automatic mapping export tools. This methodology was further revi sed and refined in this thesis. This research will not focus on analyzing or cal ibrating the model, but instead focuses on the GIS-based techniques necessary to run the m odel, and to assess the model output. The main questions that will be addressed by this research are: What steps are involved in the deve lopment of the 100-year flood model? Within this main question, related questions include: What are the steps involved in data acquisition? What are the steps involved in data pre-processing and manipulation in order to run the model? What are the steps involved to spatially represent the flood? Rational and Justification Information on floodwater inundations is of much interest to engineers, hydrologists, geologists, and geographers. Throughout history, floodplains have been developed for residential, industrial, util ity, transportation, and other businesses. Presently, floodplains in urbani zing areas have to be investigated annually or updated based on rapid changes in landuse.
4 Once the areas that are to be inundated have been determined, management plans can be developed. A risk management team could evaluate the dolla r value of benefits expected from a flood protection plan. A Best Management Plan could be put into place as a measure to hold stormwater permanently until the floodwater evaporates or infiltrates the surface. In addition, future development in areas that have been shown to inundate under the 100-year flood could be prevented if measures are taken into account to mitigate the flood hazard. Annual losses from flood could be reduced or prevented if good planning could be applied.
5 Chapter Two Literature Review Hydrologic Theories The term hydrology can be divided into tw o terms: hydro, relating to water, and loge, a Greek word meaning knowledge. T hus, hydrology is the study, or knowledge, of water (Ward and Trimble 1995). The hydrologica l cycle, which is responsible for the transport of water throughout the earths environm ent, is an essential factor in the earths climate, and for the supply of water to so ciety. Studying the water cycle, especially the terrestrial cycle, is an extr emely important topic in the ongoi ng research across the globe. Stream flow is the final connection to the hydrological cycle, which transports the water back to oceans from where it has originated. Since stream flow is routinely measured throughout the world, this data can be used to test the accuracy of climatic and hydrologic models by comparing the simulated flow to the observed flow (Lucar-Pitcher et al., 2003). In order to understand how water flows over or through a surface, scientists have to make generalizations and assumptions about reality. For instance, terrains have profound impact in hydrology. Terrain parameters such as elevations, curvature, and aspect play a key role in understanding how water flows; thus, terrain models are of interest to hydrologists. Dunne and Leopold (1978) have two different assumptions on how runoff from channels moves downward due to the topography. The first of these
6 processes is uniform, progressive flow, or translation, whereby the wave moves downstream without changing its shape (Dune and Leopold 1978). The second process operating in a flood wave is reservoir action, or pondage, whereby the wave is attenuated by storage within the channel and valley bot tom (Dunne and Leopold 1978). In Florida, the second process where storm runoff is unsteady flow is common due to the terrain. Urban areas have more impervious su rfaces than rural areas. The typical impervious areas found in urban watershed ar e roofs, sidewalks, roadways, and parking lots. The infiltration capacity of these urban features is lowered to zero. Whenever the land surface is modified due to urbanization, the runoff process will be impacted since new impervious area will be created. In addition, urban watersheds, which have a sizeable area of impervious surface, usually exhibit storm runoff rates that increase sharply during each rain period, and decrease rapidly after the rain stops. The increased storm runoff influences storm drainage cont rol, groundwater recharge, stream channel maintenance, and stream-water quality. In urban areas it is common that the runoff generally starts as overland flow on the street before entering the underground pipe system through catch pits (Mark et al., 2004). In addition to the percentage of impervi ous surface in an area, the soil moisture content also influences surface runoff. For in stance, large precipitation events following a very dry condition may produce only a small amount of runoff because of high infiltration rates, while a small event follo wing a wet period could produce a much more intense runoff because infiltration capacity woul d be low. Hydrological models are very sensitive to the hydric state of the soil; it is a key variable controlling rainfall transformation into infiltration or runoff (Aubert et al., 2003).
7 For this study, overland flow is assumed to play an important role for storm runoff as rainfall exceeds infiltration capaci ty. In certain flood models, rainfall is converted to mass runoff by using a runoff cu rve number. The curve number is based on soils, plant cover, and the amount of imperv ious areas, interception, and surface storage. This runoff is assumed to move throu gh the watershed as sheet flow, shallow concentrated flow, and open channel flow. Sheet flow flows over plane surfaces for no more than the recommended 300 feet and th en becomes shallow concentrated flow, which eventually merge to create open channel flow (USDA 1986). Runoff is determined primarily by the amount of preci pitation and by infiltration characteristics related to soil type, soil moisture, anteceden t rainfall, cover type, impervious surfaces, and surface retention (USDA 1986). In an ur ban environment, flooding may be the end result of the aggreg ation of factors. When trying to evaluate or apply knowledge of runoff responses to precipitation, we might be interested in the depth or vol ume of runoff, the peak runoff rate, or the relationship between runoff rate and time (Ward and Trimble 1995). A runoff hydrograph can be created by routing blocks of rainfall excess to the waters hed outlet. A hydrograph is defined according to Dunne and Leopold (1978) as a graph of ra te of runoff plotted against time for a point on a channel st ream flow hydrograph or hillside runoff hydrograph. The volume of flow associated w ith a storm hydrograph is equal to the sum of the rainfall excess in all the blocks of tim e associated with the storm event, multiplied by the watershed area (Ward and Trimbl e 1995). As such, hydrographs can provide researchers with vital information about th e hydrology of a small watershed. Stormwater hydrographs provide information on the change in runoff rates with time, the peak runoff
8 rate, and the volume of runoff (Ward and Tr imble 1995). One approach to developing a storm runoff hydrograph is the unit hydrogra ph method (Sherman 1932). According to Ward and Trimble (1995), the empirical appr oach is based on the assumptions that uniform distribution of rainfall excess over th e watershed, uniform rainfall excess rate, and the runoff rate is proportional to the r unoff volume for a rainfall excess of a given duration. According to Singhofen (2001) the pe ak rate factor used in conjunction with the unit hydrograph method can alter the shap e and timing of the discharge hydrograph for individual drainage subbasins. Thus, careful consideration must be taken to select the appropriate peak rate factor. Understanding the function and nature of various types of hydrograph is important in the context of this study because hydrograph are important component of the modeling phase of this project. Consequences of Flooding According to the United States Geologi cal Survey (USGS, 2005) a flood is defined as a lake, stream, or other body of water that flows over its natural confining boundaries. During a flood, water flows out over land not normally covered with water. Thus, a flood is one of the many natural hazards that have a probability of occurring. A natural hazard represents th e potential interaction between humans and extreme natural events (Tobin and Montz 1997). Once a natu ral hazard takes place, and it affects the lives of people or communities, this event can become a natural disa ster. Disasters have relatively normal occurrence over time, but it is the human as pect that truly determines how society at large will view a nd remember a given disaster.
9 Flood disasters in the United States have usually not been the most devastating disasters in terms of human life as opposed to other count ries through out the world, yet these events can significantly damage modern infrastructure such as homes, buildings and as such creates billions of dollars in damage. Agencies like the National Weather Services (NWS) which tract flood damage thr ough out the United States indicates that the cost associated with floods have been on the rise. Floods that resulted from major hurricanes can be very expensive in terms of human life. Inland flooding can be a major hazard to communities hundred of miles from the coast because of extreme rainfall volume from these vast tropical air masses. For instance, tropical storm Allison in 2001 produced catastrophic floods in Houston, Texas (NOAA 2005b). Damage estimates reported by FEMA were near $5 billion, with approximately $4.8 billion in the Houston metropolitan area alone (NOAA 2005b). In recent years, Hurricane related damage has been on the increase throughout the United St ates despite the hazard mitigation and the regulations that have been put into place (NOAA 2005c). In Florida, the summer storms associ ated with convective precipitation can generate large amount of rainfall in short ti me periods. While summer storms can cause flooding, it is the stormwater generated by hurri canes and tropical st orms that have the potential to create disastrous flooding. It is also important to point out that the wind speed of a tropical cyclone is not directly related to the amount of rainfall produced by the storm. In fact, some of the greatest rainfa ll amounts occur from weaker storms that drift slowly or stall over an area (NOAA 2005b). The year 2004 was an exceptional year in terms of flooding associated with three hurricanes that passed through central Florida. Hurricane Charley was the first hurricane to hit central Florida with maximum rainfall
10 totals being a little over 5 inches (NOAA 2005a ). The second hurricane was Frances, which produced a storm surge of nearly 6 f eet as it made landfall on the Florida east coast. Rainfalls in excess of 10 inches also occurred over large porti ons of the central and northern Florida Peninsula (NOAA 2005a). Th e third and final hurricane that ravaged central Florida was Jeanne. Widespread rainfall of up to 8 inches accompanied Hurricane Jeanne as it moved across eastern, central and no rthern Florida. A narrower band of 11 to 13 inches was observed in the vicinity of the eyewall track over Osceola, Broward and Indian River counties of east central Florida (NOAA 2005a). On their own, all of the three hurricanes were cap able of serious flooding, but it was the combination of the three storms occurring over su ch a small time interval that lead to the devastating flooding. Floods are indeed substantial natural hazards, yet communities do not always fully appreciate the threat of this hazard on the place they live. People do live in highrisk areas, and many communities are particularly vulnerable to the vicissitudes of natural events (Tobin and Montz 1997). In addition, natural disasters such as floods can critically disrupt the every day life of communities when the local inhabitants cannot return to their homes, work, or even simply their daily routines. The victims of floods may also suffer from a lot of stress associated with the disaster. Thus, the study of various flood events is importa nt, and careful research is required to prevent and/or minimize flooding hazards. In the United States as well as other countries, floods hazards may have actually been greatly exacerbated as a result of human activity. Urbanization, deforestation, and the drainage of wetlands have changed hydrologic regimes, perhaps increasing the possibility of small scale flooding (Tobin and Montz 1997). In addition,
11 flooding in urban areas can cause damage to buildings and public and private infrastructure. Because of the increased prope rty values of buildings and other structures in urban areas, flood damages can extend into the millions of dollars (Schmitt et al., 2004). 100-Year Flood Definition and Implications Large floods throughout history have b een on many occasions wrongly designated as a 100-year flood by the general public. A 100-year flood does not mean that such a flood occurs every 100 years. In reality, the 100-year flood is a flood that has a 1 percent chance of being equaled or exceeded in any given year (FEMA 2005a). In this manner, the 100-year flood could theore tically happen within the same or following years. Moreover, the 100-year flood is classified based on frequenc y and depth as are the 10year, 25-year, 50-year, 100-y ear, and the 500-year floods (Dune and Leopold 1978). In addition, it is important to note that throughout most of the world, stream flow records longer than 25 years are rare. Yet engineer s, landscape architect s, geographers, and hydrologists often need to extrapolate the volum e of rainfalls associated with 50 to 100year flood recurrence intervals. When there is a lack of historical records, researchers should recognize the dangers, and try to obtain information on such events from a variety of sources to reach a consensus (Dune and Leopold 1978). In many areas the 100-year flood contour has been delineated by FEMA. FEMA has undertaken a massive effort of flood hazard identification and mapping to produce flood hazard boundary maps, flood insuran ce rate maps, and floodway maps. Furthermore, FEMA has mandates within the National Flood Insurance Act of 1968, as
12 amended, to identify flood hazard nationwide and publish and update flood hazard information in support of the National Flood Insurance Program (NFIP). One of these areas is the Special Flood Hazard Area (SFHA), which is defined as an area of land that would be inundated by a flood having a 1% chance of occurring in any given year (FEMA 2005a). Land designated as SFHA will require permits for development. In addition, any new development will have to be raised at least one foot above the designated base flood elevation (FEMA 2005a). Floodplains just like the SFHA are another critical area in floodi ng studies. Floodplains are lo w areas susceptib le to being inundated by water of any source. Most floodp lains are adjacent to streams, lakes, or oceans although almost any area can flood unde r the right circumstances (FEMA 2003a). Flood hazards that have been mapped by FE MA or in direct cooperation and in guidance with FEMA specification are known as Flood Insurance Rate Maps (FIRM). A FIRM is a product of the flood insurance study (FIS) for a community and is available in paper or digital format. FIRMs delineate SF HAs. In addition, these FIRMs can be used as document to properly manage floodplains and assessing le vels of risk necessary for setting insurance rates. In some locations there may be very little or no land lying outside the flood zones, which poses problems for land use pla nners; in the Florida Keys, for example, there is no land available that is not subject to co astal flooding, so all development violates NFIP standards to some degree (Tobin and Montz 1997). In addition, communities, designers and builders, lenders, insurance agents, land surveyors and engineers, property appraisers and homeowners also use FIRMs. The importance of the FIRMs means that from time to time FEMA will re-evaluate whether or not a re-study needs to be conducted. As an illustration, new development can
13 significantly alter the topography and change impervious areas; thus, new flood insurance studies may be required to evaluate the ch anges of the 100-year flood and modify the FIRMs as required (FEMA 2005a). Hydraulic Models In order to better mitigate the rising cost of taxpayer funded disaster relief for flood victims and the increasing amount of damage caused by floods, the National Flood Insurance Program was put into place. The Mitigation Division, a component of FEMA manages the NFIP, and oversees the floodplai n management and mapping components of the Program (FEMA 2001a). To conduct research to determine the areas that will fall within these NFIP, FEMA has reviewed which stormwater models best meet their minimum requirements. These hydraulic models can be either one-dimensional or twodimensional. One-dimensional models use a se ries of reaches to represent the network of channels that form the waterway (FHWA 2005). Each reach is represented by a series of cross sections that includes channel and overbank geometry. The cross sections provide a 2-dimensional representation of the channe l geometry, elevation and distance. The distance between cross sections results in an overall representa tion of the waterway geometry. The model is considered one-dime nsional because the direction of flow is assumed along the channel perpendicular to the cross sections. Flow expansion and contraction occurs between cro ss sections and either flow in the vertical or lateral directions are not simulated (FHWA 2005). On the other hand, two-dimensional models use either finite difference or finite element computational methods. Models are considered two-dimensional in the sense that they compute velocity magnitude and
14 direction (two horizontal components) and ignore any vertical component of flow (FHWA 2005). According to FEMA one-d imensional models are appropriate for floodplains with substantial ove rbank storage areas and for st reams where a reversal flow may occur (FEMA 2003a). Two-dimensional co mputer models are used to simulate surface-water flows when flow could be of si gnificant importance in two directions such as in shallow flooding areas, sp lit flow situations, and at complex bridge sites (FEMA 2003a). The advantages of 1-D models are that they are re latively easy to develop and they are much faster to run. According to FHWA One-dimensional models provide excellent results for many tidal or river fl ow conditions provided that the 1-D modeling assumptions are not violated (FHWA 2005). Th e disadvantages of 2-D modeling are that they require relatively greater effort to deve lop, require more computer time to perform a simulation, and tend to have more problems with numerical instability, especially in areas of wetting and drying. As computer speeds incr ease and advances are made to network development software, these disadvanta ges become less significant (FHWA 2005). Another important factor in choosing models is whether a steady flow or unsteady flow is more appropriate for the analysis. In order to conduct analys is with unsteady flow, many assumptions are required. Th e wavelength of the disturba nce of the flow is very long relative to the depth of the flow. This shallow-water wave assumption implies that the flow is principally one-dimensional and basically parallel to the walls and bottom forming the channel (Franz and Melching 1997) Channel alignment with respect to the effect of directional changes on the conserva tion of momentum principle may be treated as if it were rectilinear even though the channel is curvilinear. In addition, the bed of the channel has a shallow slope so that the tange nt and sine of the a ngle that bottom makes
15 with horizontal have nearly the same value as the angle and cosine of the angle is approximately one (Franz and Melching 1997). The effect of boundary friction force can be interpolated with a relation derived fr om steady uniform flow. Non-uniformity and unsteadiness are assumed to have only a small effect on the frictional losses. The channel geometry is fixed so that the effect of deposit ion or scour of sediment is small (Franz and Melching 1997). Moreover, the flowing flui d is homogenous which implies constant density. Another important assumption is th at the fluxes of momentum and energy along the cross section resulting from non-uniform velocity distribution may be estimated by means of average velocities and flux-correction coefficients that are functions of location along the stream and water-surface elevation (Franz and Melching 1997). The following discussion gives an overview of hydraulic m odels that are used for one-dimensional unsteady flow and that have been ap proved by FEMA to meet their minimum requirements for NFIP. Hydrologic Engineering Centers River Analysis System (HEC-RAS) version 3.1.1 was developed by the United States Army Corps of Engineers. HEC-RAS 3.1.1 allows users to perform one-dimensional steady and unsteady flow calculations. The user interacts with HEC-RAS through a graphical user interface (HEC 2005). The system can handle a full network of channe ls, a dendritic system, or a single river reach. The effects of various obstructions such as bridges, culv erts, weirs, and structures in the flood plain may be considered in the computations (HEC 2005). Upstream boundary conditions include, flow hydrograph, stage hydrogr aph, and flow and stage hydrograph. Downstream boundary conditions consist of rating curve, normal depth, stage hydrograph, flow hydrograph, and stage and flow hydrograph (HEC 2005). Conduits can
16 be described as circular, box, arch, pipe arch, low profile arch, high profile arch, horizontal and vertical elliptical, semi-circu lar, and con/span. The data storage is accomplished through ASCII and binary files. In addition, the data can be transferred between HEC-RAS and other programs by utilizing the HEC-DSS. HEC-RAS also makes use of HEC-GeoRAS 3.1.1, which is an ArcView GIS extension that provides the user with a set of procedures, tools, and utilities for the prep aration of GIS data for import into HEC-RAS 3.1.1 (HEC 2005). The ability of HEC-RAS to be linked to the GIS allows the users to produce floodpl ain analysis fairly easily. The Full Equations (FEQ) model vers ion 9.98 is available through the U.S Geological Survey (Franz and Melching 1997). FEQ does not support a graphical user interface. FEQ is a computer program used fo r the solution of full, dynamic equations of motion for one-dimensional unsteady flow in open channels (Franz and Melching 1997). FEQ can simulate a stream system by subdividing it into stream reaches (branches), parts of the stream system for which complete in formation on flow and de pth are not required (dummy branches), and level-pool reservoirs These components are connected by special features; that is, hydraulic control structures including junctions, bri dges, culverts, dams, waterfalls, spillways, weirs, side weirs, and pumps. FEQ is written in Fortran 77 with extensions limited to those supported by most compiler but it can also read input that were based on HEC-DSS data f ile from HEC-RAS. FEQ also uses the concept of nodes and links like other stormwater model (FEMA 2001b). Three boundary conditions are used for FEQ external nodes, which include water-surface stage, disc harge, and the stagedischarge relationship. FEQ uses the St Ve nants equation. The dynamic equations of motion included in FEQ includes the integral form of equation, According to FEMA the
17 Full Equation Model has two weaknesses that must be addressed. The limitations identified are for the analysis of culver t flow (Type 5 in the USGS culvert flow classification) and modeling a floodway based on the equal conveyance reduction concept (FEMA 2001b). However, FEMA claims that this problem can be addressed by conducting extra calibration and field verification. The Storm Water Management Model (SW MM) version 4.30 is available through the EPA (EPA 1995). Its a comprehensive computer model for analysis of quantity and quality problems associated with urban runoff. SWMM is not a windows' based model, which means that the learning curve is steeper than for other models if users are only versed in GUI interfaces. Modelers can simu late all aspects of the urban hydrologic and quality cycles, including rainfall, snow melt, surface and subsurface runoff, flow routing through drainage network, stor age and treatment (EPA 1995). Flow routing is performed for surface and sub-surface conveyance and groundwater systems, including the option of fully dynamic hydraulic routing in the Extr an Block. Nonpoint source runoff quality and routing may also be simulated, as well as st orage, treatment and other best management practices (BMPs). The SWMM structured can be divided into the computational blocks and the service blocks. The computational bloc ks include the Runoff, Transport, Extran, and Storage/Treatment. The service blocks in clude Executive, Rain, Temperature, Graph, and Statistics (EPA 1995). In terms of the NFIP the Extran is indeed th e crucial block. It is in this block a network of nodes and links is represented. The links represents the conduits, channels, and streams, and the nodes are represented as junctions. Conduit elements can be computed as circular, rectangular, egg-shape, horseshoe, gothic, catenary, semi-elliptic, baskethanlde, semi-c ircular, rectangular triangular bottom,
18 rectangular bottom, trapezoid al, and parabolic (EPA 1995). The Extran block can simulate branched or looped network: b ackwater resulting from tidal or nontidal conditions; free-surface row; pressurized flow or surcharges; flow reversals; flow transfer by weirs, orifice; and pumping facilities; and storage at on-line or off-line facilities (EPA 1995). The output generated from the Extran bl ock enables the user to identify the effect of given flood scenarios. The complete flow routing uses the St. Venants equations for accurate simulation of backwater, looped connection, surcharging, and pressure flow (EPA 1995). The EPA Storm Water Management Mode l (SWMM) version 5 is a dynamic rainfall-runoff simulation model used for single event or long-term (continuous) simulation of runoff quantity and quality fr om primarily urban areas (Rossman 2004). Unlike its predecessor this new SWMM a pproved by FEMA in 2005 provides an integrated environment for editing study ar ea input data, runni ng hydrologic, hydraulic and water quality simulations, a nd viewing the results in a variety of formats. SWMM is constructed from four different compartm ents, which comprises the Atmosphere, Land Surface, Groundwater, and the Transport. The Transport compartment contains a network of conveyance elements (channels, pipes, pum ps, and regulators) a nd storage/treatment units that transport water to outfalls or to treatment facilities (Rossman 2004). The components of the Transport compartm ent are modeled with Node and Link objects. In SWMM, the conduits can be described as circular, rectangular, trap ezoidal, ellipse, arch, rectangular round bottom, egg, gothic, semi-e lliptical, and semi-circular. Three surface runoff methods are available which incl ude the Horton infiltration, Green-Amp infiltration, and the SCS Curve Number (Rossman 2004).
19 The National Weather Service (NWS) de veloped the Flood Wave routing model (FLDWAV) and was approved by FEMA in 1998 (NWS 2004). FLDWAV is a generalized flood routing progr am with the capability to model flows through a single stream or a system of interconnected wa terways (NWS 2004). FLDWAV was created to execute in the DOS environment. FLDWAV is applicable to analyze such floodplains in the context of the National Flood Insurance Program (NFIP) (FEMA 2001a). As a DOS based model, FLDWAV may be more challenging to run than models that have a GUI interface. The boundary conditions supported by FLDWAV include dams, bridges, weirs, waterfalls, and other man-made and natura l flow controls (NWS 2004). In addition, FLDWAV allows the user to select imp licit dynamic wave, explicit dynamic wave, implicit diffusion wave, or level pool solutio ns of the St.Venant equations of onedimensional unsteady flow. FLDWAV can model single channel or dendritic systems, straight or meandering cha nnels, or divided channels (NWS 2004). Some of the limitations of FLDWAV include the lack of a culvert analysis routine, it also lacks the ability to model storm sewer junctions. Th e current version of FLDWAV cannot define floodway stations based on equal conveyan ce reduction criteria. Finally, FLDWAV can analyze general riverine floodplains (natural floods), but lacks the ability to adequately to analyze non-riverine fl ood plains (NWS 2004). MIKE11 was designed by the Danish Hydraulic Institute (DHI). It is a comprehensive 1-D dynamic flow model fo r simulating hydrodynamic flows, water quality, and sediment transport in estuaries, rivers, irrigati on systems, channels, and other water bodies (DHI 2000). MIKE was designed to run in a windows environment for easy usage. According to DHI Water and Environment Hydrodynamic Module uses an
20 implicit, finite difference computation met hod for modeling of unsteady flows in rivers and estuaries (DHI 2000). This allows the m odel to be applied to branched networks, looped networks, and even quasi two-dimensi onal flow simulation such as for overbank floodplain flows. Furthermore, MIKE11 uses an integrated graphical network editor, allowing the user to quickly define a ri ver network and the associated boundary conditions. Boundary conditions are developed w ith the time series da ta prepared in the time series editor and specifica tions on locations of boundary points. River reaches can be easily defined by simply pointing and clicking with the mouse (DHI Water and Environment 200). A river reach location can be quickly changed by clicking and dragging the river reach. In addition, MIKE11 has an application that links its model results to the GIS. Once the MIKE11 simulation is complete, MIKE11 GIS can automatically generate flood maps usi ng the DEM background data and MIKE11 analysis results, allowing areas of flooding to be quickly identified (DHI Water and Environment. 2000). XP-SWMM was developed by XP-Software. Unlike EPA SWMM version 4, XPSWMM is windows based and uses a graphical expert environment to facilitate the operation of the software. XP-SWMM is a link-node model that performs hydrology, hydraulics and quality analysis of stormwater and wastewater drainage systems including sewage treatment plants, water quality contro l devices and Best Ma nagement Practices (BMP) (XP-Software 2005). Nodes represent hydraulics elements, storage, and boundary conditions. The links represen t hydraulic elements for flow and constituent transport through the system (for example, pipe, channel, pump weir, orifi ce regulator, real-time control device, etc.). XP-SW MM can characterize conduits shap e as circular, rectangular,
21 horseshoe, trapezoidal, rectangular-triangul ar, modified baskethandle, egg-shaped, catenary, semi-elliptic, gothic, semi-circular, rectangular round bottom, arch, vertical ellipse, horizontal ellipse and users can customize shape as needed. In addition, there are more than 30 different types of conduits fo r hydraulic routing. There are eleven methods available to compute storm runoff. According to XP-Software (2005) the methods include: The Non-Linear Runoff R outing (USEPA Runoff) SCS Unit Hydrograph using Curve Number with curvilinear or triangular unit hydrograph Kinematic Wave Snyder Unit Hydrograph Snyder (Alameda) Unit Hydrograph Nash Unit Hydrograph Santa Barbara Unit Hydrograph Laurensons Non-Linear Routing (RAFTS) Rational Method Colorado Urban Hydrograph Procedure (CUHP) The routing flow through storage can be perfor med by either the modified Puls method in sanitary layer or the dynamic flow equation (St Venant) in the hydraulic layers (XP Software 2005). Peak rate factor can be adjust ed from 25 to 950 with either a curvilinear or triangular unit hydrograph. Fina lly, XP-SWMM has the ability to import directly from SWMM version 4.3 and data format in ASCII text file, CSV tables, and GIS databases (XP-Software 2005).
22 The Interconnected Channel and Pond Routing model (ICPR) version 3.02 is available through Streamline Technologies. IC PR is completely menu driven with an easy to use pull-down menus uses and all data entry is stored on the fly (Streamline Technologies 2005). While traditional hydrolog ic flood routing methods work from upstream to downstream, ICPR has the ability to account for downstr eam effect at the upstream location. ICPR works on the nodelink concept. Links are the connections between nodes and are used to transfer or convey water through the drainage system (Streamline Technologies 2005). Complex draina ge networks can be modeled including dendritic (treelike), diverging, and looped systems. The storm runoff hydrograph method available in ICPR includes the SCS unit hydrograph method; the Santa Barbara urban hydrograph method, the kinematics overland fl ow method, or constructed external hydrograph files to generate hydrographs (S treamline Technologies 2005). Unlike other hydraulic models, ICPR is not limited to the peaking factor of 484. Peaking factor can be completely customized and dimensionless unit hydrograph will be generated. ICPR can route flood hydrographs through ponds, channels, and storm sewer systems. The shape of the conduits available include ci rcular, elliptical, arch, rectan gular, trapezoidal, parabolic, and irregular. A comprehensive report manager allows you to view and export an extensive array of tabular and graphical reports (Streamline Technologies 2005). The reporting system allows you to isolate specifi c areas of interest and analyze your model results quickly and efficiently. The GIS layer c ontaining information to be used has to be manually entered into ICPR as Streamline Technologies has not provi ded an easy way to capture the information from dbf format. Furthermore, ICPR can produce output results in the form of text file, which can be easil y converted to a dbf format. The ICPR model
23 was selected for use in this thesis. The m odel was run in order to demonstrate the GIS pre and post processing operations that are requ ired to set up, run, and analyze the model outputs, which is the focus of this thesis. Geographic Information Systems The use of Geographic Information Systems (GIS) has grown tremendously over recent years, and it has become a tool accepte d in many disciplines. The term GIS did not appear until the early 1960s when the Ca nada Geographic Information System was developed (Lo and Yeung 2002). However, atte mpting to define GIS can be a daunting task since GIS implies diverse meaning to different people. According to the United States Geological Survey, as sited in Lo and Yeung (2002), GIS is defined as a computer system capable of assembli ng storing, manipulating, and displaying geographically referenced information, i.e., data identified according to their locations. The word geographic implies two meanings: the Earth and geographic space. The Earth implies that all data in the system are pert inent to Earth features and resources, which also include human activities based on these features and resources. On the other hand, geographic space means that the commonality of both the data and the problems that the systems are developed to solve are geography within a specific geographical reference framework (Lo and Yeung 2002). The in formation systems part of GIS are set up to achieve the specific objectives of storing, collecting, analyzing, and presenting information in a systematic manner. Cont rary to the relatively common practice of equating spatial information systems to GIS, it is important to note th at not every spatial information system can be regarded as a GIS (Lo and Yeung 2002). As an illustration,
24 Computer-Assisted-Drafting (CAD) is not a GIS but a spatial information system. However, only those spatial information sy stems that are used for processing and analyzing geographic data (or geographically referenced data) can be labeled as GIS (Lo and Yeung 2002). In the end, GIS is unique am ong other information systems due to its geographic heritage. The geographic aspect of GIS has its roots in geography. In GIS, the real world is conceptualized as digital ge ographic data. The digital geogr aphic data can be stored either in vector or raster da ta models, which can be further analyzed and manipulated in a GIS. The vector data model is an object-based approach to the repr esentation of the realworld features. Vector laye rs depict the real world w ith lines, points, and polygons. Points are stored as x, y coordinates, and th e lines are stored as path of connected x, y coordinates while the polygons ar e stored as closed paths. On the other hand, raster data uses grids made of cell size to represent the real world. These raster layers are stored by their coordinate system in the lower-left co rner of the grid, and cell height and width, with each cell located by its row and column position. In general, raster data is more appropriate to model continuous phenomena such as elevation, wa ter table, pollution concentration, and ambient noise level. Vector data is better suited to represent discrete features with precise shapes and boundaries like land parcels, transportation, or hydrologic features. Additionally, vector and ra ster data models differ in their spatial analyses approach. Raster analyses ar e performed by conducting proximity, surface analysis, spatial tran sformation, dispersion, least-cost path analysis, and spatial coincidence. Analyses completed with vector layers are made with spatial and logical queries, layer overlay, and network analysis. Although raster and vect or data models are
25 quite different, todays computers have given GIS users the ability to simply alternate from one data model to the other. However, alternating between raster and vector data models is not always a good idea as accuracy can suffer during the conversion process; therefore, it is advised to car efully weight the benefits of switching analysis formats. Furthermore, GIS based analyses are depe ndent on the quality of the data; thus, data quality must be rigorously tested to eval uate the fitness for use. Several qualitative criteria are commonly used to describe data quality. For example, data must be reliable and accurate in order that they can be c onsidered as usable (Lo and Yeung 2002). The quality of the GIS data is largely determined by the accuracy, precision, errors, and uncertainty; therefore, it is important for GIS us ers to evaluate the fitness of the data for a given application. Data of dubious quality may actually create more problems than solve. In other words, it is imperative that geographe rs and researchers relying on GIS take into account the data quality and exercise good et hics while presenting their findings. However, all GIS based analys es are dependent on the qual ity of the data being used; thus, data quality must be rigor ously tested to evaluate their fitness for use. Data quality is a relatively abstract construct that is sometimes difficult to interpret. Several qualitative criteria are commonly used to describe data quality. For example, data must be reliable and accurate in order that they can be considered as usable (Lo and Yeung 2002). The quality of the GIS data is largely determined by the accuracy, precision, errors, and uncertainty; therefore, it is important for GIS user to evaluate the fitness of the data for a given application. Furthermore, data must also be suffici ently current and up to date for the application for which they are inte nded, as well as releva nt. In order to use geographic data with the minimum amount of uncertainty, it is imperative that
26 geographers and researchers relying on GIS ta ke into account data quality and exercise good ethics when presen ting their findings. It could be argued that a lot of the early data created in a digital format may not pass the National Map Accuracy Standards. The National Map accuracy standards were established in 1941 by U.S Bureau of the Budget to set accuracy standards for all federally produced maps. The standards were further revised in 1947 and have been the current standards ever since (Lo and Yeung 2002) To give a specific example, a digital spatial data set created at one inch equal 200 feet should be a ccurate within plus or minus three feet. In order to pass these rigorous standards it means that people creating the data must be very careful. Today, GIS is beginni ng to mature, and more emphasis has been put on documenting how the data were create d, and the accuracy of the data through documentation of metadata. Metadata is the description of the data in a data file, including data collection, sources, map proj ection, scale, quality, format, and custodian (Lo and Yeung 2002). However, the documentation of the data through the metadata is today more of luxury rather than an absolute ne cessity. This is partly due to the high cost incurred in the collection and maintenan ce of metadata information (Lo and Yeung 2002). Furthermore, it is important to note that because data is digital, it does not mean that it is good data. The United States Geol ogical Survey (USGS) for example, did not start systematic testing of ma ps until 1958. At present, accura cy testing is performed on only 10% of the mapping projects at each c ontour interval as a method of controlling overall quality (Lo and Yeung 2002). Of cour se, this does not mean that the data accuracy found in most maps would fail the Na tional Map Accuracy Standards, but that GIS users should know their responsibilitie s and follow good procedures to achieve
27 satisfactory results. Throughout the years, a staggering amount of data has been developed, but it is the accuracy of that data that is deeply concerning. GIS users who utilize data of poor quality for their analyses may not even be aware of the consequences, to least of which includes biased results. While the data may be in a digital format, geographers can still contribute to GIS by helping develop rigorous standards of data accuracy and a strong methodology. One of the truly unique abilities of GIS is to perform analyses through overlays. Spatial data in GIS can be manipulated through queries that can do a variety of operations. However, in GIS, geographic da ta may originate from multiple sources and formats. For instance, the data format may be available in dxf, dwg, coverage, shapefile, and geodatabase. It is also worth noting that converting between different formats is not a perfect process. In other words, with each conversion new errors can be introduced. Another critical aspect to GIS spatial data is topology. Topology is defined as the spatial relationships of adjacency, connectivity, and containment between geographic features (Lo and Yeung 2002). Topology is vital in GIS as features within a same layer should not overlap or have gaps between them. For example, a parcel feature that has property parcels that overlap creates a situation where th e acreage could be double counted in the overlap area, which can lead to reporting inac curate measurements. Unfortunately, not all digital formats enforce topology so GIS users have a respons ibility to test topology and ensure that the data is good a nd useable for analysis. Furthe rmore, these data sets will without a doubt contain some e rrors. If these data sets contain errors, the end product will contain errors that represent the cumula tive effect of all the errors combined. According to Lo and Yeung (2002), the accumula tion of the effect of error during the
28 process of geographic analyses is commonly referred as error propa gation. This means that as GIS users conduct their analyses the overall amount of errors will increase. As Lo and Yeung (2002) point out, the functional ity for handling attribute error propagation is obviously lacking in commerci al GIS, yet considerable effo rts have also been made to reduce these errors. Fundamentally, it is c oncerning to know that people who do not grasp error propagation may be conducting anal yses. In short, GIS analyses have great potential for research but the people using GI S must be well aware of the errors and exercise caution with the results that they present. GIS allows biologists, envi ronmental scientists, geologist, and engineers and other researchers to spatially represent their findings GIS has indeed showed its benefits as it can be used to conduct various types of analyses as well as produce maps. In environmental studies and water resources, GIS s ability to generate and display various surfaces such as Digital Elevation Models (DEM) has had a profound impact. In other words, GIS has made studies in hydrol ogy less time consuming and much more manageable. In watershed studies, DEM are the major element in non-urban environments and are especially critical in areas where the topography varies to understand the flow of water. For urban catchme nts, the flow paths can also be derived from DEM, but must be modified to account for buildings and ar tificial networks (Rodriguez et al.,. 2000). Urban catchments are often more co mplicated to generate as the stormwater information plays a vital part in the hydraulic process. Furthermore, GIS has gradually become much more important for organizations working with hydrology. In Florida, the South Florida Water Management District (SFWMD) has several projects that needs a common data model across its or ganization. Since consid erable talent and
29 time goes into hydrologic and hydraulic math ematic modeling, SFWMD has learned that GIS can significantly improve the data manage ment for such analyses (Arctur and Zeiler 2004).
30 Chapter Three Study Area Avon Park Watershed The study site is the watershed within wh ich the municipality of Avon Park is situated, which is located in the northwes tern corner of Highlands County, Florida (Figure 1). Highlands County is northwest of Lake Okeechobee and is bordered by Okeechobee, De Soto, Glades, Hardee, and Polk counties. Oliver Martin Crosby was the first to settle Avon Park in 1884, and in 1926 Avon Park officially became a city (APFLA 2005). The Avon Park watershed was sele cted for this thesis mainly because it offers the unique opportunity to conduct a flood analysis in a part of Florida which has not been extensively studied in terms of inundations by FEMA or other agencies. In addition, the Avon Park watershed is of partic ular interest to me because I have been involved with the hydrologic and GIS aspect of the project as an employee at BCI Engineers & Scientists. The climate varies from a mild dry season to a wet season. The mild dry season is typical from late fall to ea rly spring while the wet season is common from late spring to early fall. The changes in temperature are fairly moderate from the coldest season to the warmest season. The average January temperature is 63.2 degrees F, and the average August temperature is 81.8 degrees F. In terms of precipitation, the average annual rainfall from 1915 to 2004 provided by SWFWMD was 52.34 inches for the county. The
31 lowest average annual rainfall recorded wa s in 2000 at 28.98 inches. According to the historical data compiled by the SWFWMD in 1973, Highland county received an average of 77.84 inches, whic h is the maximum yearly av erage. In addition, tropical storms, hurricanes and flooding o ccur during the summer months.
Figure 1 Location Map 32
33 In 1993, three-quarters of Highlands Count y's population was in unincorporated areas. The incorporated municipality with the greatest population is Sebring with a population of 9,667 in 2000. The next most populous incorporated municipality is Avon Park, which had a 2000 population of 8,542 (US Census Bureau 2000). The population of Avon Park is more diverse than that of Florida in that 58.9% of the population was white, while the average for Florida was 78% (US Census Bureau 2000). In terms of education achievement, Avon Park ranked slightly higher than the state of Florida. In 2000, 24.4% of Avon Park population held a Bachelor degree or higher as opposed to 22.4% for the whole of Florida (US Census Bureau 2000) The median household income in Avon Park was $23,576, which is significantly lower than Florida median household income of $38,819 (US Census Bureau 2000). The per capita income of Avon Park was $11,897. The median price for vacant housing was $32,600 as opposed to Florida $92,200 (US Census Bureau 2000). Topography The Avon Park watershed is 3,285 acres. It can be noted from the topography obtained from SWFWMD that th e terrain is characterized by a gentle to moderately slopping topography. The lowest point in the study area is 100.5 feet above mean sea level (Figure 2). Ridge elevations range from 160 to 190 feet.
Figure 2 Topography 34
35 Water bodies The four major lakes that are within the watershed include lake Isis, Lake Verona, Lake Anoka, and Lake Tulane (SWFWMD 2005). Lake Isis, Lake Tulane, and Lake Verona have no outlets. Out of the four lake s within the watershed, it can be noted from table 1 that Lake Tulane is largest one with approximat ely 88.3 acres (SWFWMD 2005). The storm runoff from Avon Parks airport can drain either to a channel to the North that would discharge outside the watershed or to the South. The water that flows into the channel to the south will eventually flow through a combination of wetlands, channels, and pipe system and will ultimately discharge into Lake Anoka. The SWFWMD topography was used to determine the eleva tion at which overland flow would occur should the lake basins be completely fille d. Should the surface water of Lake Anoka reach 126 feet, the water would travel through a channel and discharge into Lake Lelia. Table 1: Summary Statistics of the Lakes Lakes Acres Overflow Elevation (ft) Lake Isis 51.2 126 Lake Verona 38.4 140 Lake Anoka 44.8 126 Lake Tulane 83.2 125 Source: SWFWMD 2005 Soils The most dominant soils in the watershe d as can be noted from table 2 are the Astatula urban land complex (33.4%), Astatu la sand (20.2%), Tavares sand (18.0%), and Basinger fine sand (12.8%) (SWFWMD 2005). In addition, hydrologic group A accounts for 71.6% of all the hydrologic groups. While the core of the watershed is spatially dominated by hydrologic group A, the western portion of the waters hed is dominated by
36 hydrologic group B/D and C. The water bodies by themselves occupy 5.5% of the total watershed (SWFWMD 2005). Table 2: Summary Statistics of the Soil Types Soil Acres Percent of Total Astatula sand/0 to 8 percent slopes 770.9 20.2 Astatula-urban land complex/0 to 8 percent slopes 1277.7 33.4 Basinger fine sand 490.1 12.8 Placid fine sand/depressional 7.3 0.2 Pomello sand/0 to 5 percent slopes 226.0 5.9 Satellite sand 132.2 3.5 Tavares sand/0 to 5 percent slopes 689.9 18.0 Urban land 20.6 0.5 Water 210.9 5.5 Grand Total 3825.6 100.0 Source: SWFWMD 2005 Table 3: Summary Statistics of the Hydrologic Soil groups Hydrologic Group Acres Percent of Total A 2738.5 71.6 B/D 490.1 12.8 C 358.2 9.4 D 28.0 0.7 W 210.9 5.5 Grand Total 3825.6 100.0 Source: SWFWMD 2005
Figure 3 Soils Map 37
38 Landuse The Avon Park watershed is characterized as 58% urban (table 4). The urban areas are located within the core of the city as depicted in figure 4. The second most important land use is agriculture, which cove rs 22.8% of the watershed. The agriculture land cover is found for the most part around th e south west corner of the watershed and around the edge of the study area. Another significant land use is the transportation, communication, and utilities which covers 8.4% of the study area. The municipalitys airport which is located in the western part of the city limit occupies a large portion of this land use. Table 4: Summary Statistics of the Landuse types Landuse Acres Percent of Total Urban 2220.2 58.0 Agriculture 872.1 22.8 Rangeland 61.1 1.6 Upland Forest 98.3 2.6 Water 230.0 6.0 Wetlands 21.6 0.6 Transportation, Communication & Utilities 322.4 8.4 Grand Total 3825.6 100.0 Source: SWFWMD 2005
Figure 4 Landuse Map 39
Chapter Four Methodology Data Sources This thesis focuses specifically on how GIS played an integral role in modeling the 100-year flood. The methodology section will detail all the GIS steps necessary in data acquisition, data creation and manipulation in order to get to the point where the data can be imputed into the ICPR model (figure 5). The Results and Discussion section will explore the GIS post-processing procedures that take place once the model has been run and the floodplain delineation procedure. Figure 5 Methodology 40
41 The study relies in part on a detail modeli ng approach to determine the areas that would be inundated based on the 100-year flood event in the Avon Park watershed (figure 6). The first step was the watershe d evaluation and parameterization, where in a systematic inventory, assessment and subsequent development of the water resource features was conducted. The watershed evalua tion consisted of gathering, and in some instance developing, all available data releva nt to the study area. The data that were gathered includes topographic elements, elements from aerial photographs, landuse, soils, and rainfall (table 5). The data that were developed using GIS consisted of subbasins, hydraulic features (bridge, c ontrol structures, culverts, overland weirs), the hydraulic network, flow lines, cross section, subbasins stage/storage, and a digital terrain model (table 6). Once all the elements necessary fo r the model were gather ed and generated, a professional engineer modeled the 100-year flood using ICPR. The final step of this analysis involved the floodplai n delineation. It is at this stage that a GIS procedure was put into place to nearly automate the floodplain delineation process. Table 5: Data Acquisition Data Type Source Purpose Aerials LABINS Subbasins delineation, Landuse SWFWMD Model parameters Soils SWFWMD Model parameters Rainfall SWFWMD Model development Elevation Contours SWFWMD DTM development, model parameters Spot Elevations SWFWMD DTM development Source: SWFWMD 2005
42 Table 6: Data Creati on and pre-processing Data Type Purpose Water bodies DTM development, subbasins delineation Watershed boundary DTM development DTM Model parameters Subbasins Model parameters, model development Basincov Model parameters Hydraulic Inventory Model development Hydraulic Network Model development Flow lines Model parameters Cross Sections Model development Source: SWFWMD 2005 In order to conduct a watershed study, it is common to amass a large set of data pertaining to the area in questi on. Essentially all the elements required for this analysis were obtained from the Southwest Florid a Management District (SWFWMD), Land Boundary Information System (LABINS).
Figure 6 Study Area 43
44 Watershed Evaluation and Parameterization A Digital Terrain Model (DTM) was create d for this study. All elevations data used in this analysis were in Nationa l Geodetic Vertical Datum (NGVD) 1929. The DTM was built from contours, spot elev ations, water bodies, and the watershed boundary. The contours provided by SWFWMD had one-foot intervals based on a 1989 survey by Continental Aerial Survey, Inc. However, as depicted in figure 7, one-foot contour was not available in four areas of the watershed. The areas to the North and North-West are the largest areas where the detailed contour data was missing. In order to cover the entire watershed, the United Stat es Geological Survey (USGS) five-foot contour obtained from SWFW MD was used in the areas where one-foot topography was missing. The areas in and around the city of Avon Park have not experienced any major topographical changes since 1989; therefore, the five-foot topography should provide adequate results for this analys is. Figure 8 illustrates the four elements that were utilized for the creation of the DTM. The spot elevatio ns which are digitally represented as points were developed by Continental Aerial Surve y, Inc in 1989. The water bodies represent the lakes and ponds found inside the watershed. The water bodies were delineated on top of the 2004 aerial topography. The approximate elevation of the water bodies was determined from the spot elevations when available or the elevation of the nearest contour was assigned. The watershe d boundary depicts the study area. The DTM was built in the form of a Tr iangulated Irregular Network (TIN). The TIN was assembled in ArcToolbox. The termi nology used for the TIN is the one that corresponds to the Environmen tal Systems Research Institutes (ESRI) ArcGIS TIN software. The contours were entered as hardli nes. The spot eleva tions were entered as
45 mass points. The water bodies were entere d as hardreplace. Finally, the watershed boundary was entered as a hardclip. By this process a TIN was constructed for the Avon Park watershed.
Figure 7 1989 One-Foot Contours 46
Figure 8 Digital Terrain Model Development 47
48 Aerial photographs of th e study area were obtained from LABINS. These aerials are remotely sensed images in which displacem ent of features in the images causes by terrain relief and sensor orie ntation have been mathematically removed. In other words, these aerials combine image characteristics of photographs with geometric qualities of maps. The aerial imageries are true color a nd were flown in 2004 shortly after the study area was impacted by hurricanes. The aerial pho tographs feature a one -pixel resolution. The accuracy has been designed to meet th e National Map Accuracy Standards (NMAS) for 12,000 maps. In other words, at 12,000 scale th e accuracy is expected to be plus or minus 33.33 feet. The 1999 soil coverage was acquired fr om SWFWMD. This coverage was updated with the 2004 aerial photographs. The updates focused on updating any areas where new water bodies may have been cr eated since 1999. As an illustration, it is common for developers to create detention or retention ponds in new subdivisions. The updating process was conducted with ArcGIS editing tools by either recoding the attributes and/or modifying the existing so ils boundary. Next ESRI geoprocessing wizard was used to perform a clip overlay. A clip operation will cut out a piece of one layer using another polygon in another layer as a co okie cutter. Figure 3 displays the soil coverage clipped by the watershed. The landuse coverage was also obtain ed from SWFWMD. This coverage represents the 1999 condition and the 2004 aeria l photographs were used to update this coverage. The most important changes that were targeted involved the water bodies and new urban areas. The landuse coverage was mo dified and updated with ArcGIS editing tool by either recoding the landuse or re shaping the boundaries between different
49 landuse. Next, the geoprocessing wizard was used to do a clip overlay between the landuse and the watershed boundary as the clipping tool. Figure 4 illustrates the landuse that was updated and clipped with the watershed boundary. The rainfall station coverage as well as the spreadsheet containing the historical rainfall distribution was obtai ned from SWFWMD. There are five rainfall stations located in the vicinity of A von Park (figure 9). Only one station had real time data available (ROMP 43XX (SWFWMD station 413) ). The real-time rainfall data was downloaded from the SWFWMD real-time da ta site. ROMP 43XX rainfall station is located northeast of Avon Park. During this study, Hurricane Jea nne struck central Florida on September 26, 2004. From the results of the statistical analysis of the rainfall data, Hurricane Jeanne produced rainfall for th e City of Avon Park, of 6.21 inches in a 24-hour period. This is a 24-hour rain fall event from 9pm on Sept 25 th to 9pm on Sept 26 th (figure 10). As it can be noted from tabl e 7, this rainfall event corresponds to a 10year 24-hour event for Highlands County.
Figure 9: Rainfall Stations 50
Table 7: Highlands County Rainfall Frequency Amount Frequency (Years) Period of Rainfall Rainfall Amount (inches) 2 year 24 hours 2.33 year 24 hours 4.5 5 year 24 hours 5.4 10 year 24 hours 6.3 25 year 24 hours 7.5 50 year 24 hours 9 100 year 24 hours 9.5 500 year 24 hours 11.3 10 year 5 day 10.4 50 year 5 day 13.8 100 year 5 day 15.5 500 year 5 day 20.8 Source: SWFWMD 2005 Hurricane Jeanne00.20.40.60.811.21.4024681012141618202224Time (hours)Rainfall Amount (inches) Figure 10: Hurricane Jeanne Rainfall Distribution 51
52 The subbasins that are within the study area were delineated in ArcGIS with the editing tools based on the available one-f oot and the five-contours and the 2004 aerial photographs. Ridge lines, hydraulic control poin ts, storage areas, and local collection networks were features that define a subbasin boundary. The subbasins delineation approach followed closely the 2004 Southw est Florida Water Management District Guidelines & Specifications. A depression that is one acre or greater in size and has an associated depth of 2-feet or more, had it s the contributing area broken out as its own subbasin (SWFWMD G & S 2004). Furthermore, local conveyance or collection systems (man-made channels, washes, etc.) that have a contributing area greater than or equal to 40 acres before discharging to a significant hydraulic control featur e were broken out as subbasins. Storage areas such as lakes, wetlands, ponds, and hydrated stormwater management storage areas that ar e greater than or equal to fi ve acres were broken out as a unique subbasin due to their uniform hydr ology and their effect on direct runoff (SWFWMD G & S 2004). Moreover, the hydrauli c control features govern subbasins in urban areas; therefore, the subba sins in urban areas were broken out into more detail than areas that are more rural. Finally, it is impor tant to point out that certain subbasins were hard to finalize due to topographic voids. Topographic voids occur whenever the land cover has changed or when 1-foot topogra phy is unavailable. As required in the SWFWMD G & S 2004, a field reconnaissance was conducted to corroborate the subbasins where topographic voids exist. Figure 11 illustrates the subbasins for the watershed.
Figure 11 Avon Park Subbasins 53
The 2004 aerial photographs were utilized to identify the possible hydraulic structures located throughout the watershed. The locations of hydraulic structures like bridges, control structures, culverts, and overland weirs that are pertinent to the model were identified on maps for use in field reconnaissance. Figure 12, 13, and 14 show some of the control structures located during the field reconnaissance. The hydraulic structures positions were recorded using a Global Positioning System (GPS). Figure 15 illustrates the hydraulic structures captured for the model. The GPS unit uses the signal correction known as Wide Area Augmentation System (WAAS). The unit is expected to produce three-meter accuracy roughly 95 percent of the time. Hydraulic structure parameters such as the size, shape, and amount of culverts were inventoried at this stage. Figure 12 Lake Verona 54
Figure 13 Dry Channel Figure 14 Airport Ditch 55
56 The pictures that were taken during th e field reconnaissanc e were used in conjunction with the USGS report written by Gillen (1986) to assign Mannings n for the channels. All the channels modeled with IC PR were compared to the channel picture exhibit found the in USGS report and the Mannings n was then assigned.
Figure 15 Hydraulic Inventory 57
58 Once the subbasins and hydraulic featur e inventory had been finalized, a hydraulic network represented w ith junction and reach was cr eated using ArcGIS editing tools (figure 16). Hydraulic reaches are represen ted as arcs in the junction/reach coverage while the nodes are represented as the termin al ends of the arcs. Arcs represent the reaches (links) such as culverts, weirs, and channels while the nodes represent the upstream and downstream junctions that typi cally connect to other reaches (SWFWMD G & S 2004). Nodes are discrete locations with in the watershed used to define inflow points, boundary conditions, storage areas, changes in channel slope or geometry, or any other points of interest. St orage areas include depressi on and water bodies. Moreover, urban areas differ from rural ar eas as junction are more likely to be placed at man made hydraulic structures such culverts, control st ructures while juncti ons location in rural areas are placed in natural water re source areas (SWFWMD G & S 2004).
Figure 16 Hydraulic Network 59
In order to acquire the watershed parameter assignments a combination of GIS operations and spreadsheet manipulation were required. The time of concentration is another essential component for the model. Figure 17 illustrates a flow diagram of the GIS process in order to calculate the time of concentration. Figure 17 Time of Concentration The first step to acquire the time of concentration was to create flow lines by digitizing them with ArcGIS editing tool. The flow lines were digitized from the most hydraulically distant point of the watershed to a point of interest in the watershed The approach to generate time of concentration was based on the Technical Release 55 (TR-55) available through the United States Department of Agriculture (USDA 1986). The first 300 feet were sheet flow and; the rest of the flow lines were classified as shallow 60
61 concentrated flow. The flow lines are shown figure 18. The flow line coverage was edited in ArcGIS where sheet fl ow were attributed with the name of the subbasins plus the letter A while the shallow concentrated flow were attributed with the name of the subbasin plus the letter B. By assigning a letter in addition to the subbasins name, the sheet flow and shallow concentrated can be distinguished through a simple query. Next, the flow lines were converted to a point feature class (figure 19). The TIN for Avon Park was converted using the ArcToolbox TIN to grid operation to a one pixel cell size resolution Digital Elevation M odel (DEM). ESRI Spatial Analyst was used to conduct a zonal statistics operation. A zonal statistics operation provided st atistical information regarding elevation at each poi nt. Through this process, the range in elevations of the sheet flow and the shallow concentrated flow were obtained. Next, the slope for the sheet flow and shallow concentrated flow was calculated based on the length and range in elevations.
Figure 18 Flow Lines 62
Figure 19 Flow Lines Converted to Points 63
64 Once the range in elevations, length, a nd the slope of the flow lines were finalized, the results were br ought into Microsoft Excel wher e a template was ready to bring in the GIS results to calculate the time of concentration. In short, the key elements that were required from the GIS were the creat ion of sheet flow and shallow concentrated with their associated length, ra nge in elevations and slope. The other GIS operation that was required for the calculati on of the time of concentration involved an overlay between th e subbasins, landuse, and soils coverage. ESRI geoprocessing wizard was used to conduct two union operations. A union operation will generate an output that will contain the attributes of the layers being combined. The first union combined the subbasins with so il layers which created an output that contained both soils and subbasi ns. Next a union overlay be tween the soils and subbasins output with the landuse was conducted to produc e the final output that contains the subbasins names, soils hydrologic soils group, and landuse classifica tion by fluccs code, and acreage attributes. The combined results of subbasins, soils and landuse generated in ArcGIS were imported into Microsoft Excel where a template containing predefined curve numbers and Mannings friction f actor were assigned. The initial Soil Conservation Service Curve Numbers were assigned by land use classification and hydrologic soils group shown in table 8. Th e curve number is a measure of runoff potential that has values of 30 to 100. Th e curve number was calculated from soil and land-use type. For each subbasin the curve num ber was calculated by using the weighted average of pervious and impervious area curve number.
65 Table 8: Watershed Parameter Assignments Curve Number Hydrologic Soil Group FLUCCS Generalized Landuse Description A B B/D C D W 1100 Residential-Low Density 39 61 61 74 80 99.8 1200 Residential-Med Density 39 61 61 74 80 99.8 1300 Residential-High Density 39 61 61 74 80 99.8 1400 Commercial / Institutional 39 61 61 74 80 99.8 1500 Industrial 39 61 61 74 80 99.8 1700 Commercial / Institutional 39 61 61 74 80 99.8 1800 Recreation / Open Space 39 61 61 74 80 99.8 1900 Recreation / Open Space 39 61 61 74 80 99.8 2100 Agriculture Pasture / General 39 61 80 74 80 99.8 2200 Agriculture Citrus 32 58 79 72 79 99.8 2400 Agriculture 67 78 89 85 89 99.8 2600 Agriculture Open Space 39 61 60 74 80 99.8 3100 Herbaceous Rangeland 39 61 80 74 80 99.8 3200 Shrub Rangeland 30 48 73 65 73 99.8 4100 Forest 32 58 79 72 79 99.8 4110 Forest 32 58 79 72 79 99.8 4200 Forest 32 58 79 72 79 99.8 4340 Forest 32 58 79 72 79 99.8 5200 Water 99.8 99.8 99.8 99.8 99.8 99.8 5300 Water 99.8 99.8 99.8 99.8 99.8 99.8 6150 Wetland 98 98 98 98 98 99.8 6300 Wetland 98 98 98 98 98 99.8 6400 Vegetated Non-Forested Wetlands 98 98 98 98 98 99.8 6410 Wetland 98 98 98 98 98 99.8 6430 Wetland 98 98 98 98 98 99.8 6440 Wetland 98 98 98 98 98 99.8 6520 Wetland 98 98 98 98 98 99.8 8100 Transportation / Utilities 83 89 89 92 93 99.8 8300 Transportation / Utilities 83 89 89 92 93 99.8 Source: USDA 1986 The curve number assignments from tabl e 8 as well as the initial Mannings n (friction factor) and Directly Connected Impervious Area percentage (DCIA%) found in the Technical Release No 55 were assigned fr om table 9. This step was conducted by using Microsoft Excel basic function such as looking values with the vlookup function which can be used to retrieve data from different spreadsheets quickly.
66 Table 9: Watershed Parameter Assign ments Mannings n and Percent DCIA FLUCCS Generalized Landuse Description Manning's N DCIA (%) 1100 Residential-Low Density 0.16 20 1200 Residential-Med Density 0.13 25 1300 Residential-High Density 0.08 50 1400 Commercial / Institutional 0.05 85 1500 Industrial 0.07 72 1700 Commercial / Institutional 0.13 65 1800 Recreation / Op en Space 0.13 10 1900 Recreation / Open Space 0.3 0 2100 Agriculture Pasture / General 0.15 0 2200 Agriculture Citrus 0.3 0 2400 Agriculture 0.2 10 2600 Agriculture Open Space 0.15 0 3100 Herbaceous Rangeland 0.3 0 3200 Shrub Rangeland 0.3 0 4100 Forest 0.45 0 4110 Forest 0.45 0 4200 Forest 0.45 0 4340 Forest 0.45 0 5200 Water 0 100 5300 Water 0 100 6150 Wetland 0.3 100 6300 Wetland 0.3 100 6400 Vegetated Non-Forested Wetlands 0.06 100 6410 Wetland 0.06 100 6430 Wetland 0.06 100 6440 Wetland 0.06 100 6520 Wetland 0.06 100 8100 Transportation / Utilities 0.15 25 8300 Transportation / Utilities 0.15 25 Source: USDA 1986
67 Table 10: Watershed Parameter Assignments Basin Name Area (acres) Curve Number Manning's N DCIA (%) TC (minutes) A1030 50.44 33 0.27 1 52.9 A1035 30.55 39 0.13 53 24.7 A1039 32.19 39 0.12 50 34.1 A1045 79.73 39 0.10 52 67.2 B2040 31.21 34 0.28 0 46.0 C3010 46.75 99 0.00 99 10.0 C3020 29.68 43 0.20 16 38.7 C3030 91.03 42 0.12 34 70.5 C3040 42.70 39 0.11 37 42.3 C3050 30.09 40 0.30 0 52.1 C3055 90.92 52 0.09 52 56.3 C3060 91.73 37 0.19 25 56.8 C3070 82.12 53 0.24 12 84.3 C3075 20.82 35 0.24 0 72.4 C3080 10.71 67 0.22 25 38.9 C3090 205.27 58 0.30 7 99.9 C3095 18.20 81 0.19 12 50.7 C3100 66.31 76 0.23 12 57.4 C3102 1.93 92 0.15 25 10.0 C3110 156.59 53 0.16 27 70.4 C3120 49.97 77 0.12 40 39.9 C3125 475.69 56 0.19 16 55.2 C3130 76.88 74 0.14 32 62.1 C3140 88.52 86 0.16 23 96.5 C3145 14.32 64 0.22 14 49.9 C3150 9.90 84 0.15 25 24.2 C3155 16.74 90 0.15 25 25.2 C3160 9.03 89 0.15 25 150.6 C3165 7.76 92 0.15 25 54.9 C3170 5.33 92 0.15 25 22.0 C3175 13.96 71 0.22 14 50.0 D4010 87.89 98 0.00 97 10.0 D4020 24.19 45 0.12 33 29.1 D4030 37.46 37 0.18 8 38.4 D4040 35.87 37 0.17 19 35.3 D4050 63.75 39 0.14 27 32.4 D4060 44.00 39 0.12 34 29.0 E5010 41.01 99 0.00 98 10.0 E5020 166.77 39 0.12 35 64.6 E5030 67.33 39 0.13 29 34.5 E5040 31.37 36 0.21 5 38.8 E5050 17.22 40 0.14 17 29.3 Source: USDA 1986
68 Table 10: Watershed Paramete r Assignments (continued) Basin Name Area (acres) Curve Number Manning's N DCIA (%) TC (minutes) E5060 180.15 40 0.12 39 59.2 E5070 37.40 39 0.14 21 31.9 F6010 50.28 99 0.00 98 10.0 F6020 45.31 36 0.20 16 29.8 F6040 96.26 38 0.17 21 45.5 F6050 169.29 40 0.11 43 70.1 F6055 212.04 39 0.19 25 110.1 G7020 52.68 38 0.15 28 35.0 G7030 65.77 36 0.17 29 42.7 H8020 181.72 46 0.27 10 39.4 H8030 140.85 39 0.12 51 101.7 Source: USDA 1986 In brief, to calculate the time of conc entration found in table 10, it was imperative to have the following GIS layers: subbasins, la nduse, soils, flow lines, and a DEM. Next, a series of GIS operations such as overlays and zonal statistics were required. Finally, the results generated in ArcGIS were brought into Microsoft Excel where the calculation of the time of concentration was made based on the curve number assignments from table 8 as well as the initial Manni ngs n (friction factor) and Di rectly Connected Impervious Area percentage (DCIA%) from table 9. The results shown on table 10 were then imported directly into the ICPR model through a si mple copy/paste operation. The study area was surveyed by Kendric Land Surveying. The surveying company was contracted to survey all the hydraulic structures a nd channels that are pertinent for the ICPR model. The exact channe l width and elevations were surveyed. In addition, the exact size inverts of the pipes were surveyed. In the ICPR model, cross section profiles for the channels were represen ted as irregular, which is appropriate if the channel cross sections have been surveyed or when fine resolution sources such Light
Detection and Ranging (LIDAR) is available. In addition to the channels profiles, it was necessary to acquire the profile around the overland weirs. The location and cross sectional information of the overland weirs allowed for model simulation of flow over ridges or roads (termed Pop-offs). In the event of a significant storm like the 100-year event, culverts could become saturated or storage areas might reach their maximum water detention, which is why it is important to represent the areas where the storm runoff would flow. The cross section of the overland weirs was determined using survey data and the available topographic data. The cross section that was used in the model is the combination of the survey data and the 1-foot or 5-foot topographic data that were derived using the EZ Profiler v8.3 tool in ArcGIS. Figure 20 illustrates an example of a cross section profile. Cross Section 50701421441461481501521541561580500100015002000Station (feet)Elevation (feet) Figure 20 Cross Section Profile 69
70 The EZ profiler v8.3 was obtained from ESRI and is available for free. This tool works similarly to the profile tool found in ESRI Spatial Analys t and 3D Analys t where a line can be digitized and the software will gene rate a table containing coordinate points and elevations. The order of the overland cross se ctions was from left to right looking from the upstream side of the cross section as required in the SWFWMD G & S (2004). On figure 21, the channel cross sections and overl and cross section lines are displayed. The overland cross sections have a lot of curvature because they closely follow where stormwater could overtop a nd leave a given subbasin.
Figure 21 Cross Sections 71
72 Subbasin storage calculations were de veloped by using the DTM and Arc Macro Language (AML) codes that were provide d by SWFWMD. AML is a high-level, algorithmic language that provides macro-progr amming capabilities that can be used to automate routine processes within ARC/INFO. In order to have the AML work, three components are needed. The first component is a coverage named basincov. The basincov coverage was created from the subbasin coverage. While the subbasins coverage was broken into storage and contri buting areas, the basinc ov coverage contains only storage as the contributing areas have been dissolved using ArcGIS editing tools so that only storage areas are represented. As an illustration, Figure 22 shows the subbasins contributing to Lake Tulane, and figure 23 shows the storage area for Lake Tulane. In other words, the contributing areas to the lakes were combined because they are the storage areas. The second component is th e topography. The topography coverage was labeled topo as the AML is looking for th is specific coverage name for the topography. The final component of the AML is text file. The purpose of the text file is to indicate which storage needs to be calculated.
Figure 22 Lake Tulane Subbasins 73
Figure 23 Lake Tulane Storage 74
75 In addition, to the three components me ntioned previously, there are two AML scripts that had to be used in order to calcu late the stage/storage volume. The first AML script utilized the three components to ge nerate TINs per storage area. Figure 24 illustrate the TIN that was created for Lake Tulane storage area. The second script requires the text file with th e storage name and the storage TINs that were created with the first AML script. This script actually calculated the storage volume at one foot intervals within each TIN and produces a text fi le that contains that data. The volume for each storage area was then simply copied and pasted into ICPR.
Figure 24 Lake Tulane Storage TIN 76
77 Data Input into the model While large amount of data acquisition and data mani pulation was required using GIS, only a few key GIS elements are actually needed for the model. These include the nodes, reaches, hydraulic invent ory, cross sections, subbasin na mes, acres, and storage. The GIS data needed for the model was imputed into ICPR, and then an engineer set up the model. The model was run, verifi ed, calibrated and approved by SWFWMD. Hurricane Jeanne which corres ponds to the 10-year flood was utilized to verify and calibrate the model. Table 11 illustrates how the model simulated Hurricane Jeanne Event which occurred on September 26, 2004. Three of the four lakes found inside the watershed had lake stage available. By co mparing post Hurricane Jeanne stage from the model max stage we can notice how close th e simulated model results matched to the actual lake stage. Table 11: Model Verification Us ing Hurricane Jeanne Event Lake Name (Node Name) Period of Record (POR) Pre Hurricane Jeanne Stage (feet) (Sept 23, 2004) Post Hurricane Jeanne Stage (feet) (Sept 29, 2004) Model Initial Stage (feet) Model Max Stage (feet) Lake Anoka (NC3010) 06/1981 04/2005 123.86 125.01 123.86 124.956 Lake Tulane (ND4010) 06/1981 04/2005 115.38 (09/20/04) 116.1 115.38 116.162 Lake Verona (NE5008) 11/1981 04/2005 114.94 116.08 (10/18/04) 114.94 117.452 Source: SWFWMD 2005
78 Chapter Five Results and Discussion Model Results As discussed in the methodology, GIS played a key role in the processes used to create, manipulate and store all the elements required for using a hydraulic model to determine the 100-year flood. In addition to data acquisition, and pre-processing and manipulation, model development and verification can be extensive. This research does not focus on all the detailed aspects that we nt into the model veri fication and calibration, instead it focuses on the aspects of data acqui sition, pre-processing of data necessary to set up the model, and post-proces sing of model outputs in order to most effectively depict and analyze model output. Figure 25 illustrates the overall process to produce the 100year floodplains with ArcGIS.
Figure 25 Floodplain Delineation In order to generate the 100-year flood, the 100-year flood 24 hour event and the 100-year flood five day event were generated. As previously illustrated on table 7, the 100-year flood 24 hour event was based on 9.5 inches of rainfall, while the 100-year flood five day event was based on 15.5 inches of rainfall. Table 12 presents the simulated maximum water surface elevation at the nodes, which were generated from the model and brought into Microsoft Excel so that the results could be further explored and queried. One might expect the five day storm to produce a higher maximum water surface elevation at the nodes compare to the 24 hour storm since the rainfall volume is larger for the five day storm. For the most part the water level was higher for the five day storm, with the exception of nodes NA1038, NA1039, and NA1045. I believe that different landuse and soil characteristics may have had an effect on those results. 79
80 Table 12: 100-Year 24 Hour and 5 Day Model Results Node Name Subwatershed 100-Year 24 Hour Water Surface Elevation (feet) 100-Year 5 Day Water Surface Elevation (feet) NA1010 Lotela 107.804 109.054 NA1030 Lotela 157.705 159.818 NA1038 Lotela 150.174 150.129 NA1039 Lotela 142.523 142.403 NA1045 Lotela 130.635 130.562 NB2010 Lelia 116.997 119.683 NB2031 Lelia 119.658 120.354 NB2032 Lelia 124.493 125.561 NB2035 Lelia 121.853 122.228 NB2040 Lelia 130.276 132.103 NC3010 Anoka 126.480 126.825 NC3040 Anoka 165.045 165.164 NC3051 Anoka 143.148 143.208 NC3052 Anoka 143.674 143.894 NC3053 Anoka 143.137 143.199 NC3060 Anoka 141.932 141.964 NC3074 Anoka 145.510 145.423 NC3075 Anoka 147.730 148.073 NC3076 Anoka 144.407 144.413 NC3080 Anoka 146.304 147.025 NC3083 Anoka 146.867 146.673 NC3090 Anoka 146.322 147.040 NC3095 Anoka 147.151 147.483 NC3096 Anoka 148.822 149.013 NC3097 Anoka 152.006 153.268 NC3100 Anoka 152.065 153.305 NC3101 Anoka 152.432 153.444 NC3102 Anoka 152.634 153.454 NC3105 Anoka 152.294 153.440 NC3120 Anoka 153.510 153.550 NC3122 Anoka 150.176 150.184 NC3125 Anoka 147.270 147.270 NC3130 Anoka 153.735 153.731 NC3140 Anoka 154.295 154.293 Source: SWFWMD 2005
81 Table 12: 100-Year 24 Hour and 5 Day Model Results (continued) Node Name Subwatershed 100-Year 24 Hour Water Surface Elevation (feet) 100-Year 5 Day Water Surface Elevation (feet) NC3145 Anoka 155.797 155.876 NC3150 Anoka 153.953 154.817 NC3155 Anoka 153.723 153.784 NC3160 Anoka 154.268 154.266 NC3165 Anoka 154.157 154.155 NC3170 Anoka 153.204 153.454 NC3175 Anoka 156.107 156.097 NC3177 Anoka 152.139 153.352 ND4010 Tulane 116.775 118.025 ND4060 Tulane 136.188 136.174 NE5008 Verona 119.517 123.770 NE5070 Verona 142.875 142.950 NF6010 Isis 111.797 115.686 NF6050 Isis 147.506 147.613 NG7020 Viola 142.127 142.321 NG7030 Viola 129.209 129.273 NG7050 Viola 108.000 108.000 NH8020 Damon 101.000 101.000 NH8030 Damon 121.435 121.420 ND4010 Tulane 116.775 118.025 ND4060 Tulane 136.188 136.174 NE5008 Verona 119.517 123.770 NE5070 Verona 142.875 142.950 NF6010 Isis 111.797 115.686 NF6050 Isis 147.506 147.613 NG7020 Viola 142.127 142.321 NG7030 Viola 129.209 129.273 NG7050 Viola 108.000 108.000 NH8020 Damon 101.000 101.000 NH8030 Damon 121.435 121.420 Source: SWFWMD 2005 In order to generate the 1 00-year flood it was necessary to compare and select the maximum water surface elevation from the 24 hour and 5 day events at the nodes. The results from the two events were explored and queried with Microsoft Excel. The query involved selecting the maximum water surface elevation of the 24 hour and 5 day events and a new result table was created (table 13). Furthermore, the premise behind the 24
82 hour and 5 day event modeling is that the 24 hour event will address the peak rate sensitivity, while the 5 day event will ad dress volume sensitivity (SWFWMD G & S 2004). In this regard, the 100-year flood should th erefore address the peak rate sensitivity and the volume sensitivity.
83 Table 13: 100-Year Max Stage Node Name Subwatershed 100-Year Max Stage Water Surface Elevation (feet) NA1010 Lotela 109.054 NA1030 Lotela 159.818 NA1038 Lotela 150.174 NA1039 Lotela 142.523 NA1045 Lotela 130.635 NB2010 Lelia 119.683 NB2031 Lelia 120.354 NB2032 Lelia 125.561 NB2035 Lelia 122.228 NB2040 Lelia 132.103 NC3010 Anoka 126.825 NC3040 Anoka 165.164 NC3051 Anoka 143.208 NC3052 Anoka 143.894 NC3053 Anoka 143.199 NC3060 Anoka 141.964 NC3074 Anoka 145.510 NC3075 Anoka 148.073 NC3076 Anoka 144.413 NC3080 Anoka 147.025 NC3083 Anoka 146.867 NC3090 Anoka 147.040 NC3095 Anoka 147.483 NC3096 Anoka 149.013 NC3097 Anoka 153.268 NC3100 Anoka 153.305 NC3101 Anoka 153.444 NC3102 Anoka 153.454 NC3105 Anoka 153.440 NC3120 Anoka 153.550 NC3122 Anoka 150.184 NC3125 Anoka 147.270 NC3130 Anoka 153.735 NC3140 Anoka 154.295 Source: SWFWMD 2005
84 Table 13: 100-Year Ma x Stage (continued) Node Name Subwatershed 100-Year Max Stage Water Surface Elevation (feet) NC3145 Anoka 155.876 NC3150 Anoka 154.817 NC3155 Anoka 153.784 NC3160 Anoka 154.268 NC3165 Anoka 154.157 NC3170 Anoka 153.454 NC3175 Anoka 156.107 NC3177 Anoka 153.352 ND4010 Tulane 118.025 ND4060 Tulane 136.188 NE5008 Verona 123.770 NE5070 Verona 142.950 NF6010 Isis 115.686 NF6050 Isis 147.613 NG7020 Viola 142.321 NG7030 Viola 129.273 NG7050 Viola 108.000 NH8020 Damon 101.000 NH8030 Damon 121.435 ND4010 Tulane 118.025 ND4060 Tulane 136.188 NE5008 Verona 123.770 NE5070 Verona 142.950 NF6010 Isis 115.686 NF6050 Isis 147.613 NG7020 Viola 142.321 NG7030 Viola 129.273 NG7050 Viola 108.000 NH8020 Damon 101.000 NH8030 Damon 121.435 Source: SWFWMD 2005
85 Delineation In the past, floodplain boundaries were manually delineated using topography, cross sections, and aerial photographs. Some hydraulic models have developed tools to automate the delineation process. MIKE 11 by DHI Water and Environment and HECRAS version 3.1.1 developed by the United Stat es Army Corps of Engineers have the ability to generate floodplain inundation files useable in the GIS. However, the ICPR model from Streamline Technologies, Inc. that was used for this analysis does not have the ability to delineate the floodplain in ArcGIS or other GIS software. The procedure discussed below will explain how the model results were brought into GIS. This procedure originated fr om Watershed Concepts. The procedure by Watershed Concepts involved the setup of mapping cross sections which were represented as polylines. The advantage of th is method is that it only requires some manual work in the initial setup. Once the manual work has been completed any events modeled can be represented in the GIS. In ot her words, this method is useful for models that do not have automatic mapping export tools. After getting familiar with the Watershed Concepts procedure, it became clear that the procedure could be automated a nd refined further. While the Watershed Concepts procedure only involved the placement of mapping cross sections, the technique that was used for this thesis involved the setup of mapping polygons. The two techniques produced nearly identical results, but the technique presented in this thesis reduced the manual setup considerably. In or der to begin this de lineation procedure two feature classes are necessary. The first feat ure class is the mapping cross sections which is a polyline feature class. The mapping cross sections should not be confused with the
86 channel and overland cross sections created for the model development as the mapping cross sections is a unique f eature class with the sole purpose of mapping the 100-year flood. The mapping cross sections are suitable for flow-ways such as channel systems and they will not be used for storage areas. The mapping cross sections were placed at the location of each node where water surface elevations in the channel systems were calculated in the hydrau lic model. The second feature class that was cr eated is the mapping polygons. In order to create the mapping polygons, the storage coverage that was used previously for the stage-storage calculation was once again utilized. A GIS operation involving an inside buffer of one foot was performed using ArcGIS buffer wizard. Next, using X-tools Pr o, an erase operation was perf ormed with storage coverage as the input and the one foot buffer coverage as the erasing polygons which produced an output that was named Mapping polygons. The final step was to remove the area of conveyance by performing a query to identify the conveyance areas and eliminate these from the mapping polygon coverage. The ma pping polygons were used to represent storage such as lakes, ponds, and wetlands ar eas and other areas that act as storage. The mapping polygons were attributed with the st orage node of the resp ective storage area that they represent. Figure 26 illustra tes the mapping cross sections and mapping polygons in the southwest corner of the watershed. As a side note, it is important to point out that the watershed is dominated with the mapping polygons since storage subbasins are prevailing throughout the watershed with the exception of a few conveyance systems found in the South-West corner of the watershed and North of the airport.
Figure 26 Mapping Cross sections and Mapping Polygons 87
88 The simulated 100-year flood results presen ted in table 13 were saved to a dbf format. This dbf was brought into the GIS where its attributes were joined to the mapping polygons and cross sections by using a table join operation. Because a table join is not permanent, a field called flood wa s added to the mapping polygons and cross sections and they were both attributed with the maximum water surface elevation using a calculate value operation which is similar to a copy and paste done in Excel or other software. The next step in the delineation proce ss involved the creation of a Triangulated Irregular Network (TIN) to spatially repres ent the maximum water surface for the 100year flood (figure 27). ESRIs 3D Analyst was used to create the TIN. The mapping cross sections were imputed as hardline. The reason for imputing the mapping cross sections as hardline was to create a natu ral slope within the channel systems. The mapping polygons were imputed as hard replace. The idea behind imputing the mapping polygons as hard replace was to assign consta nt elevation to boundary and all interior heights. ESRI ArcToolbox was then used to convert the maximum water surface elevation TIN to a one cell-size resolution Digital Elev ation Model (DEM) by using the TIN to grid tool. Using ESRI Spatial Analyst, a query was conducted to determine where the water surface elevation DEM was greater than th e actual terrain el evation. The output produced a grid that was coded with 0 and 1 (figure 28). The areas coded with a 1 signify that the maximum water surface elevation was greater than the actua l terrain surface; while the 0 value means that the maximum water surface elevation did not exceed the terrain elevation. Thus, the areas coded as 1 are actually the areas inundated during the
89 100-Year flood. Using ESRI Spatial Analyst, the convert raster to feature operation was selected which produced a vector feature of the flood. Once the grid was converted to a vector format, all the areas coded with a 0 were excluded using an attribute query, which delineated the areas that would be flooded in the event of the 100-year flood based on the simulation. Figure 29 illustra tes the spatial representati on of the 100-year flood. Based on these results, the flood would be fairly extensive around the airport (Figure 30), and numerous depressional areas within the ci ty would be also inundated (figure 31 and figure 32). Additionally, the st orage of stormwater runoff by the lakes would increase. Based on the simulation, Lake Anoka is the only lake that would actually overflow into other areas potentially causing property dama ge and other problems for the people living in this general area.
Figure 27 Maximum Water Surface Elevation TIN 90
Figure 28 Flooded and Non Flooded Areas 91
Figure 29 100-Year Flood 92
Figure 30 100-Year Flood Around the Airport 93
Figure 31 100-Year Flood Around Lake Anoka and Lake Tulane 94
Figure 32 100-Year Flood Around Lake Isis and Lake Verona 95
96 Reviewing the floodplain delineation procedure The procedure utilized in this thesis minimized the amount of manual work for floodplain delineation. As previously discussed, the maximum water surface elevation was modeled in ArcGIS by utilizing the mapping cross sections and polygons. The mapping polygons were used for the storage ar eas, while the mapping cross sections used for conveyance. Once the mapping polygons an d mapping cross sections are finalized, any model results, even for different recurrence interval events lik e the 500-year flood, could be easily represented. The benefit of using the mapping cross sections for the conveyance is that it allows the software to create a gradual slope which provides a much more realistic and natural re presentation of the channel sy stems. Had mapping polygons been used in lieu of mapping cross sections the flood depth would have possessed a stairstep effect with sudden changes in the water surface elevation de picted within the conveyance. A stair-step effect is genera lly only acceptable in locations where hydraulic control structures or natural waterfalls exis t. The storage areas were represented with mapping polygons which produced a smooth water surface. An advantage of the mapping polygons is that because they are imputed as a hard replace, the software will not interpolate elevation within the storage area, bu t rather keep a constant elevation. This is important because the maximum water surface el evations are represented for all storages, hence it is critical that th e elevation is not interpolated and remains constant. For example, a hard replace is commonly used in GIS for the construction of a TIN for water bodies such as lakes, because they have a set elevation. The study area has not been completely mapped by FEMA (figure 33). In fact, most of the core of Avon Park had never been mapped, yet a small area along Lake
97 Anoka and wetlands nearby have been mapped as zone A which indica tes that this area was delineated using an approximation met hod (Figure 34). According to FEMA, no detailed hydraulic analyses were performed and no base flood elevations of flood depth were determined in zone A (FEMA 2005b). While the area mapped as zone A was completed through approximation, it offers an in sight into flooding in the area. Figure 34 depicts the simulated 100-year flood produced by the ICPR model overlaid with the 1996 FEMA flood zones. While the 1996 FEMA fl ood zones can only be used as a general guide, there is a similar trend around Lake Anoka.
Figure 33 1996 FEMA Flood Zones 98
Figure 34 Simulated 100-Year & 1996 FEMA Flood Zones 99
100 Another interesting alterna tive to simply mapping the extent of the flood is to spatially represent the flood in terms of depth. In order to spatially represent the depth associated with the 100-year flood, the maximu m water surface eleva tion and the digital terrain elevations are necessary. Similar to mapping the flood extent, the flood depth is calculated using ESRI Spatial Analyst. The firs t step in this procedure was to select the 100-year flood extent as a mask in the Spatial Analyst option, which operates as a cookie cuter operation similar to a c lip operation used in vector analyses. Next, the raster calculator was used to conduct a spatial query invol ving the subtraction of the digital terrain model elevation from the maximum water surface. A flood depth grid was generated from this operation that depicts the flood in terms of depth within the extent of the 100-year flood. Figure 35 illustrates the flood depth associated with the 100-year flood. By spatially representing the flood depth, it is possible to acquire a better understanding of potential damage from the 100-year flood.
Figure 35 Flood Depth Associated With The 100-Year Flood 101
102 Another point worth noting is the importance of the storage being as representative to the real world as possible. Because the mapping polygons were initially based from the storage, it was critical that this coverage adequately represent the realworld hydrologic system. In other words, th e representation of th e 100-year flood is only as good as the data which it was based on. Furthermore, as mentioned in the methodology section, the watershed was not completely covered by one-foot topography, and some areas of the DEM where built using the USGS five-foot topography (figure 7). While the 100-year flood was delineated thr ough out the watershed, it is important to point out that the areas of five-foot topography are lacking the details found within the one-foot topography because five-foot topography is not appropriate for low relief areas like Florida. Hence, caution should be used when interpreting th e results based on the five-foot topography.
103 Chapter Six Conclusion Summary and General Procedure The primary goal of this thesis was to docum ent the integral role that GIS plays in modeling the 100-year flood and the watershed for Avon Park. In this regard, this research contributed to the existing pool of knowledge by demonstrating how GIS can be used in this type of research. GIS has benefited many disciplines through its unique ability to spatially represent data and to conduct analyses. Hydrology is without a doubt one of the many disciplines that has benefited significantly from GIS. The steps involved in the ac quisition of data requir ed gathering existing GIS vector data from SWFWMD, while the GIS ra ster data were obtai ned from LABINS. The data that was gathered includes t opographic elements, aerial photography, landuse, soils, and rainfall. Next, this GIS data was evaluated for its suitability for use in the study, with another ongoing aspect of th e data acquisition in volving updating the database if better data becomes available. For example, the 1999 aerials photographs were originally used as part of the study, but in 2004 new aerial were made available through LABINS, and they were then incorporated in the resear ch. Because data acquisition was an essential component of this research, it was imperative that the best available data were included in this research.
104 Once all the necessary data was acquired, th e next steps of this research focused on data pre-processing, generation, and manipul ation. It was at this point that the watershed study actually began because all the n ecessary data were in place. At this time new GIS data were generate d such as water bodies, TIN, DEM, subbasins, storage, watershed boundary, hydraulic inventory, hydr aulic network, flow lines, and cross sections. During this stage, the spatial data base was supplemented with field work in order to most accurately digitally represent the Avon Park watershed. Once all the data was ready to be imputed into the model, a professional engineer ran, verified, and calibrated the model so that the 100-year flood could be simulated. As previously discussed, ICPR is a model th at does not have the ability to directly represent its simulated flood results spatially. In order to spatially represent the 100-year flood, the model results from th e 100-year 24-hour and 5-day events were queried with Microsoft Excel to the simulated 100-year flood. Some additional GIS data were generated with the mapping polygons and cro ss sections where the model results were attributed to these spatial layers. A series of GIS ope rations were conducted which resulted in the final delin eation of the 100-year flood. Method Suitability The methodology utilized for this thesis was geared toward gathering, creating, and processing the GIS data so th at it could be used for the IC PR model. Of course, there are numerous other models that would m eet FEMAs minimum requirements, as indicated in the literature review section with the eight hydraulic models briefly described in this thesis. In addition to meeting FEMA s minimum requirements, the model selected
105 for this research had to also satisfy SW FWMD minimum requirements as well. For example, Hec-Ras model did not meet the SWFWMD requirements because it could not easily use the 256 unit hydrograph. The N RCS method was the preferred method to calculate the subbasins runoff. The ICPR model was selected because of its runoff methods, manageable interface, and suitability for Florida. While the methodology used in this research provides detailed information on the required data for ICPR, a large portion of th e data set could also be used for other models. For example, the topography, field reconnaissance, subbasi n delineations and the collection of landuse, soils, and rainfall distribution are all important elements for modeling. Obviously, the method to calculate subbasin runoff will vary from model to model, but the technique highlighted in this research would benef it other models that make use of the NRCS method. In terms of spatially representing the flood, different models may require the mapping polygons and cross sections to be set up differently. In other words, representing the flood spatially requires a basic understandin g of the modeling approach, and especially the intent behind how the ba sins were delineated, and how the hydraulic network was constructed. As a whole, the development of the 100-year flood model required a significant number of steps to be performed using GIS. As discussed, other models may require different steps to be conducted to build a hyd raulic model, but I be lieve that the steps presented in this thesis provide a solid me thodology to successfully integrate GIS with the model development. Furthermore, the st eps involved in the data acquisition can be applied to virtually any wate rshed located in the Southwes t Florida Water Management
106 District, and the other water management dist ricts through out Florida. While the source of the data may originate from different s ources in other states, the steps in data acquisition are expected to be similar. Th e steps involved in data pre-processing and manipulation in order to run the ICPR mode l, can without a doubt, be applied to other study areas. Of course, the data pre-proces sing and manipulation would be different with digital data that is LIDAR based, but once agai n the overall steps can easily be applied to other study areas. In terms of data post-processing, the steps di scussed in this thesis can generate solid spatial representation of the flood. Obviously, different models may require some modifications to the technique presented in this thesis, but this can be addressed by understanding the intent behi nd the development of the subbasins and hydraulic network put into place. Future Studies The steps outlined in this thesis with resp ect to the use of GIS as a tool in model pre and post-processing are app licable to many of the mode ls. By documenting all the necessary steps related to data acquisition, data processing and manipulation, the model interface and GIS, and the post processing of the model results, this thesis can serve as a resource for future studies that utilize bot h GIS and hydraulic modeling software. While the results from this analysis are not absolute, they do enable us to get an understanding of the areas that are prone to flooding fr om the simulated 100-year flood in the Avon Park Watershed. Furthermore, a vast porti on of Avon Park had never been mapped in terms of the 100-year flood. For many resi dents who are located within the 100-year flood, this analysis offers them the benefit of knowing that they are in a flood prone area.
107 As an illustration, the people who live far from water bodies might believe that they are not vulnerable to flooding; yet this analysis has shown otherwise. With this information, governments agencies and local entities coul d implement stormwater management plans to lessen the impact or prevent a future inunda tion. In other words, this analysis provides the information about the extent of the fl ood and by this token; the residents could perhaps take measures and perhaps be better prepared in the event of a flood disaster. While this study was centered on modeling the 1 00-year flood, additional research can be undertaken based on this thesis. In a ddition, once the mapping cross sections and polygons have been set up, any events simulated can be mapped. Therefore, engineers and geographers could evaluate how different flood events would impact the Avon Park watershed. Additional research such as a level service anal ysis could be conducted which could provide detailed information about the impacts of the flood on the roads and local infrastructures. Modeling the 100-year flood event is a process that requires continued experimentation and the creati on of new methods or revision of older methods to produce more accurate and precise modeling. While the GIS procedures utilized in this thesis were good and efficient, future research c ould improve the GIS procedures by automating more of GIS processes. For example, th e subbasins were delineated manually, but in ArcGIS version 9.1 or higher, ESRI Spatial Analyst could be used to automatically delineate the subbasins based on a DEM. Of course, automating the subbasins delineation will not produce the perfect subbasins as there are important variables besides topography such hydraulic struct ures that determine the delineation of the subbasins. Nonetheless, by automating the subbasins delineation process, a lot of time will be saved
108 that could be applied toward quality check and quality control. In addition to subbasins delineation being automated, the flow lines which were created for the time of concentration could be genera ted through ESRI Spatial Analys t in ArcGIS version 9.1 or higher. Finally, the GIS procedures employe d in this thesis could be revised and automated further through ESRI Modelbuilder which was first made available in ArcGIS version 9.0. ModelBuilder is an interface used to build and edit geoprocessing models in ArcGIS. ModelBuilder offers the possibili ties to improve the GIS work-flow as the entire GIS procedures could be setup and run utilizing ModelBuilder. By this token ModelBuilder would remove re petitive task and permit a more efficient work-flow. The 100-year flood had never been fully mapped for the core of the municipality of Avon Park (figure 33). I believe that by mapping the 100-year flood in Avon Park, valuable information was obtained so that soci ety can better prepare in the event of such a powerful storm. In addition, based on this analysis hydraulic models and GIS can be integrated successfully, but it is evident that data quality is a concern. Data sources, especially in digital form may be available in various digita l formats which often lead to substantial manual conversions of data. Collecting the data requi red to run a hydraulic model is a time-consuming task, and the data ha s to be created or transformed in order to be used which is why data quality is also of great concern. As an illustration, the topography utilized for this thesis was acceptable, but Light Detection and Ranging (LIDAR) would have been far more accurate and precise. In addition, new topography generated from LIDAR would, without a doubt, replicate th e terrain better than the previously available topography data and it wo uld also better indi cate the changes that may have taken place. For example, Florid as topography is very gentle, which is why
109 even small changes introduced from de velopment may have significant impacts on a given watershed. Unfortunately, LIDAR is ve ry expensive and it is unlikely that small cities like Avon Park will be able to afford data products of this quality. Beyond the use of LIDAR, another area for improvement is th e inventory of hydraulic structures. Over the next few years, as more data becomes digital, perhaps more information regarding sewers or other important hydraulic structures could be inventoried in spatial databases and incorporated in the mode l. In the end, while current modeling techniques are good, future research will be improved with wider availability of high quality digital data, as well as improvement in the integration of GIS and hydraulic modeling techniques.
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