ABSTRACT. Watershed-scale Distributed Hydrologic Modeling and Assessment of Low Impact Development Features in White Oak Bayou, Houston, TX

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2 ABSTRACT Watershed-scale Distributed Hydrologic Modeling and Assessment of Low Impact Development Features in White Oak Bayou, Houston, TX by Christina M. Hughes This thesis proposes a method for modeling site-scale Low Impact Development (LID) features at the watershed scale to evaluate the as-yet unknown performance of LID in a high intensity rainfall region. Increased impervious cover from urban development causes increased peak flows at the watershed outlet during rainfall events, often leading to flooding. Although LID features have been constructed across the U.S. to address this issue, their performance has not been evaluated in a high intensity rainfall region or cumulatively in a large watershed. Using the fully-distributed Vflo hydrologic model of the White Oak Bayou watershed in Houston, TX, two common urban retrofit LID features were modeled (rain gardens and green roofs) using a simple parameter-averaging method for various frequency storms. Findings indicate that although unable to significantly control the 100-year storm event, LID features can effectively reduce outlet discharges during smaller storms when fully implemented across a large urban watershed.

3 Acknowledgments Special thanks to Dr. Philip B. Bedient for your support, guidance, and freedom of direction on this project, and for providing me with the invaluable opportunity to connect with community partners through research. Thank you to Dr. Baxter Vieux for providing access to the Vflo model and for your prompt feedback, valuable insight, and enthusiastic assistance on developing the modeling methodology for this project. Thanks also to Dr. Qilin Li for agreeing to serve on my committee and for providing me with excellent background and guidance from which to shape my analysis. Special thanks to Dr. Jeffrey Nittrouer for your mentorship, patience, for broadening my perspective, and for encouraging my enthusiasm for scientific research. I would not have had the courage or endurance to finish this project without the continuous support of my research colleagues Toni Sebastian, Tatyana Luttenschlager, Ben Bass, Nick Irza, Courtney Hale, Jessie Gill, Katherine Anarde, Mikaela Mahoney, Larry Dunbar, Nick Fang, and the Sed Sisters. Special thanks to Jacob Torres and Andrew Juan for allowing me to interrupt you constantly for guidance and feedback. Thank you to members of the White Oak Bayou Association and TIRZ 5 for background information on the project and support for this research. Finally, I would like to express thanks to my family and friends for their unconditional love and support, and to the countless others who have leant me their time, resources, opinions, and expertise during this process.

4 Contents Acknowledgments... iii Contents... iv List of Figures... vi List of Tables... viii List of Equations... ix Nomenclature... x Introduction Low Impact Development Hydrologic Modeling Lumped Hydrologic Models Distributed Hydrologic Models Summary of Objectives Model Background and Application General Description of the Vflo Model White Oak Bayou Study Area Flood Control in White Oak Bayou Base Model Development Model Application Sensitivity Analysis Model Calibration Hurricane Ike (11.2 in/48 hrs) July 2012 Storm (5.3 in/72 hr) September 2013 Storm (4 in/24 hr) LID Modeling Methodology LID Model Development LID Feature Modeling Assumptions Modeling Rain Gardens as Residential LID Features Modeling Green Roofs as Commercial and Public LID Features Results and Discussion LID Performance for Design Storms... 72

5 v 4.2. LID Feature Parameter Analysis The Effects of Spatial Rainfall Distribution Distribution Analysis of Subcatchment Response Conclusions Major Findings Future Work References Appendix

6 List of Figures Figure Vflo Geospatial Data Inputs Figure 2.1.2: Vflo Grid-cell System and Cell Components Figure 2.2.1: Relative Location of the White Oak Bayou Watershed, Houston, Texas Figure White Oak Bayou Watershed and Elevation Map Figure White Oak Bayou 10-year Floodplain Showing Inundated Structures Generated from HCFCD 2010 HEC-RAS Model E Figure Base Model Sensitivity Analysis Results Figure Rain and Stream Gauge Networks and Subcatchment areas in and around White Oak Bayou Watershed in Houston, TX Figure Spatially-distributed Rainfall Totals (inches) over White Oak Bayou during Hurricane Ike Figure Calibration Results for Hurricane Ike at Lower White Oak Bayou ( ) and the Outlet ( ) Gauges Figure Calibration Results for July 2012 Storm at Lower White Oak Bayou ( ) and the Outlet ( ) Gauges Figure Calibration Results for September 2013 Storm at Lower White Oak Bayou ( ) and the Outlet ( ) Gauges Figure Cottage Grove Rain Gardens Post-construction Figure Visual Representation of Rain Garden Infiltration Modeling Technique Figure Extensive Green Roofs on the NASA Johnson Space Center and Rice University Campuses Figure Cumulative Hydrologic Performance of LID Scenarios A-C for 10-to 2-Year Design Storms Compared at the Watershed Outlet ( )

7 vii Figure Hydrographs of the Outlet Response under Base and LID Scenario C to the 100-year and 2-year Design Rainfalls Figure Scenario LID C Reductions in Peak Discharge for 6- and 24-Hour Storms of Equivalent Frequency Figure Sensitivity Analysis of LID Feature Design Parameters by Land Use Figure Model Sensitivity Analysis of Initial Saturation under LID Scenario C and 10YR/24HR Rainfall Conditions Figure Comparison of Historic (Sept. 2013) and Theoretical (SCS Type III) Temporal Rainfall Distributions for a 4 in/24 hr Storm in White Oak Bayou Figure Comparison of LID Scenario C Reduction in Peak, Volume, and Timing of Runoff Between Spatially-Uniform and Spatially-Distributed Storm Events Figure Spatial Distribution of Total Rainfall During September 2013 Storm Event in White Oak Bayou Watershed Figure Map of Cole Creek, Brickhouse Gully, and Little White Oak Bayou Subwatersheds and their Respective Stream Gauges Figure Proportion of Land Use Areas Associated with LID Features under the RCP1 LID Development Scenario in the White Oak Bayou Watershed, Cole Creek, Brickhouse Gully, and Little White Oak Bayou Subwatersheds Figure Hydrologic Performance of LID Scencario C for 10- to 2- Year Design Storms Compared at the Tributary Outlets

8 List of Tables Table Major Return Period Rainfalls for Northwest Harris County, Texas.. 32 Table Vflo Input Data and Sources for Development of White Oak Bayou Base Model Table Green and Ampt Soil Parameters for White Oak Bayou Soils Table Overland Roughness Parameters Based on Land Cover Classifications adapted from Kalyanapu et al Table Historical Rainfall Events Used for Calibration of the White Oak Bayou Base Model Table Summary of Errors in Volume and Peak Flow for Final Calibrated Base Model at all Stream Gauges for Observed Data During Four Storm Events in White Oak Bayou Table Rain Garden Design Parameters Table Green Roof Modeling Parameters Table hour Design Storm Results for LID Scenario C Table LID Scenario C Hydrologic Impact Reductions for the 10-year/24- hour Storm Event at Each Subcatchment in White Oak Bayou Watershed

9 List of Equations Equation 2-1 Kinematic Wave Analogy Continuity Equation Equation 2-2 Manning s Equation for Overland Flow Equation 2-3 Green and Ampt Infiltration Equation Equation 2-4 Manning s Equation for Open Channel Flow Equation 3-1 General Weighted Parameters Cell-Averaging Equation Equation Weighted Parameters Cell-Averaging Equation for Rain Garden Infiltration Parameters Accounting for Disconnection of Impervious Area Equation 4-1 Percent Reduction in Peak and/or Volume of Discharge

10 Nomenclature DEM HCFCD KWA LID LiDAR MAPE USACE USGS Digital Elevation Model Harris County Flood Control District Kinematic Wave Analogy Low Impact Development Light Detection and Radar Mean Absolute Percentage Error U.S. Army Corps of Engineers United States Geological Survey

11 Chapter 1 Introduction Land development and urbanization drastically alter the hydrologic response of a watershed to rainfall. Increased impervious cover, compacted soils, cleared wetlands and forested areas, and a concentration of anthropogenic pollutants often lead to decreased environmental health and a heightened flood risk (Leopold, 1968; Arnold & Gibbons, 1996; NRC, 2008). Traditional urban drainage solutions, such as pipes, gutters, and ponds, have historically been unable to effectively address both water quality and quantity within the limited available space in an ultra-urban watershed. A concept known as Low Impact Development (LID) is being increasingly utilized in cities across the U.S. to simultaneously address both the hydrologic and water quality impacts of urbanization. LID practices encourage natural stormwater management processes such as infiltration, filtration, and evapotranspiration. Part of the LID theory includes the substitution of conventional stormwater management structures for engineered infiltration-based landscape features, such as rain gardens or green roofs. 11

12 12 This thesis proposes a method for incorporating site-scale LID features into watershed-scale models and uses this method to quantify LID feature hydrologic performance in the high intensity rainfall region of Houston, Texas. Motivating questions include (1) what is the best method to model small-scale infiltration-based LID over a large area, (2) what cumulative impact do LID features have on downstream flow behavior during high-intensity rainfalls, and (3) what factors of LID feature implementation drive significant variations in discharge reduction during various frequency storm events. The following study utilizes a GIS-based distributed hydrologic model of a highly developed watershed in Houston, Texas to answers these questions and to assess the practicability of LID retrofits for flood control in the Gulf Coast Region Low Impact Development Watershed hydrologic response is typically measured at a gauged location in the main drainage waterway at the downstream end of a watershed. Hydrologic impacts are quantified using three metrics: the peak level of discharge observed during a given storm event, the total volume of runoff drained into the channel, and the time at which the peak discharge occurs. A significant body of literature exists that confirms that increased impervious area from development and urbanization causes an increased peak and volume of stormwater runoff (Hollis, 1977; Jennings & Jarnagin, 2002; Waananen, 1969), decreased time to peak (Leopold, 1968), downstream erosion, and overall water quality degradation (Desai, et al., 2010; Makepeace, et al., 1995; USEPA, 1983).

13 13 Conventional approaches to mitigating increased runoff volume include structural drainage infrastructure improvements such as pipes, gutters, and lined or dredged canals. Although effective at transmitting rainfall runoff downstream and away from the source area during a storm event, these systems contribute to a flashier hydrology, whereby increased peak flow and decreased time to the observed peak in the downstream receiving bodies lead to flash flooding. These systems typically do not reduce runoff volume or water quality impairments. Volume abstraction and water quality enhancement processes such as infiltration, adsorption, and filtration are prevented by the low-friction impervious networks which make up conventional drainage systems. An alternative method for urban drainage which addresses these issues more holistically has recently emerged. Low Impact Development (LID), also known as green infrastructure, has been gaining popularity over the past two decades as an unconventional stormwater management strategy in urban areas of the United States. However, LID performance under various climate and environmental conditions is still not yet fully evaluated (Coffman, 2002). Low Impact Development was pioneered in the early 1990 s in Prince George s County, Maryland and describes alternative stormwater management practices that promote enhanced infiltration, groundwater recharge, and water quality improvement to mimic pre-development hydrologic conditions (USEPA, 2000; Ahiablame, et al., 2012). Ideally, LID is a land development strategy aimed at conserving and protecting existing ecologically important features and minimizing directly-connected impervious surfaces (USEPA, 2000; 2014). Studies have shown that watershed planning practices

14 14 initially developed using this strategy, such as preservation of riparian corridors, conservation of existing wetlands, and implementation of neighborhood-scale control features, can effectively manage extreme storm events and resemble the predevelopment hydrologic regime, in terms of rainfall runoff and channel discharges, more closely than conventional developments (Doubleday, et al., 2013; Zimmerman, et al., 2010; Selbig & Bannerman, 2008; Bedan & Clausen, 2009). What is less well known, however, is how LID can create a hydrologic landscape that is functionally equivalent to predevelopment conditions when implemented post-development as a series of distributed micro-scale engineered landscape features, such as rain gardens and green roofs (USEPA, 2000). LID has gained popularity for its effectiveness in site-scale retrofits to improve watershed health and address existing issues with development, especially in urban and residential areas where land availability and greenfield development are limited (USEPA, 2000; Coffman, 2002; Abi Aad, et al., 2010; Damodaram, et al., 2010). Although retrofit LID features tend to be smaller and more distributed than centralized conservation-based features, such as wetlands or riparian corridors, the concepts of source control, pollutant removal, and runoff reduction are still inherent in their design and functionality. Sitescale LID features commonly studied and implemented across the U.S. include bioretention (rain gardens), green roofs, and rainwater harvesting systems (rain barrels and cisterns). With the exception of rainwater storage tanks, these features typically consist of surface depressions with a highly vegetated top layer to restrict flow and a high permeability soil media layer to encourage infiltration. They are often located directly downstream of impervious areas in order to intercept runoff at the source.

15 15 Rain gardens, also known as bioretention, are among the best studied and most widely used LID features for both new developments and urban retrofits across the U.S. (USEPA, 1999; Davis, et al., 2009; Abi Aad, et al., 2010; Ahiablame, et al., 2012). These features typically comprise a landscape depression with high permeability soil (often engineered) and a mixed vegetation cover, typically consisting of native or climatetolerant plants chosen to optimize pollutant removal. Although initially designed as water quality control features (Davis, et al., 2009), rain gardens have been shown to function as stormwater quantity controls in temperate environments by providing increased roughness and infiltration capacity to reduce volume and slow timing of runoff (Ahiablame, et al., 2012). Studies in various regions and temperate climates have shown that rain gardens are most effective at reducing runoff associated with small storm events, and have a significant impact when appropriately sited to directly intercept runoff from impervious surfaces (Hunt, et al., 2006; Gilroy & McCuen, 2009; Abi Aad, et al., 2010; Ahiablame, et al., 2012). Reductions as high as 97% have been recorded for low intensity rainfalls in the Northeast and Pacific Northwest (Chapman & Horner, 2010; DeBusk & Wynn, 2011; Davis, et al., 2012). Rain gardens are generally recommended as single-site urban or residential stormwater controls and not for use in flood control (USEPA, 1999; Gilroy & McCuen, 2009). However, a review by Davis et al. (2009) hypothesized that when implemented across a larger watershed, rain gardens could have a significant cumulative impact on downstream flows. Despite the more recent and intentional use of rain gardens and rainwater harvesting for stormwater management, green roofs (also known as vegetated roofs) have been used extensively for the past 40 years to absorb the impacts of rainfall, to regulate

16 16 indoor temperatures through increased building insulation, and to provide an urban aesthetic (USEPA, 2000; Mentens, et al., 2005). In the past few decades, green roofs have become one of the most popular stormwater management practices in cities across the U.S. and are among the first LID features to begin appearing in the Gulf Coast Region, despite the lack of data on green roof performance in sub-tropical climates (USEPA, 2000; Ahiablame, et al., 2012). The basic concept behind a green roof is replacement of a traditionally impervious roof with a pervious vegetated area planted above a waterproof membrane. The most common and easiest retrofit style in the U.S. is the extensive roof, whereby the vegetated covering consists of six inches or less of growing substrate (USEPA, 2013). Typical green roofs are planted with a variety of native, drought-tolerant plants. Rooftop irrigation systems can also be included in the design, but generally decrease the stormwater retention capacity of the substrate due to higher volumetric water content (Volder & Dvorak, 2014). Studies indicate that performance varies, but generally decreases with increasing rainfall volume and intensity due to a limited soil infiltration rate and storage capacity (Dietz, 2007; Volder & Dvorak, 2014). Depending upon the level of maintenance and seasonality, green roofs can be expected to retain anywhere from % of rainfall volume for a given storm event (Ahiablame, et al., 2012; Hathaway, et al., 2008; Bliss, et al., 2010; Carpenter & Kaluvakolanu, 2011; Stovin, et al., 2012; Voyde, et al., 2010). For small storm events (less than 0.4 inches), extensive green roofs have been shown to be extremely effective at reducing runoff by retaining approximately % of water during a single event (VanWoert, et al., 2005; Carter & Rasmussen, 2006; Getter, et al., 2007). A recent study by Volder and Dvorak (2014)

17 17 measured a green roof runoff volume reduction of 30-50% during larger storm events (up to 4 inches in depth). Minimal data currently exist on the water quality enhancement that green roofs provide since they typically do not intercept runoff from impervious areas and therefore do not provide pollutant filtration. In fact, there is some indication that the vegetated covering and substrate composition may actually export nutrients and contribute to downstream water quality degradation (Hathaway, et al., 2008; Hutchinson, et al., 2003). Despite the limited data on water quality or hydrologic performance of green roofs in high intensity rainfall climates, they are a popular retrofit due to the added insulation and reduction in the heat island effect provided by the soil and vegetation covering (USEPA, 2013). Watershed-scale implementation of green roofs has not been evaluated from a hydrologic perspective, but nevertheless should theoretically reduce the cumulative runoff volume downstream from individual rainfall events and provide lower water temperatures and slower velocities to prevent channel scour and degradation (USEPA, 2013). Of the LID retrofit features currently studied and implemented across the U.S., green roofs are among the first to be implemented in the City of Houston and are currently located on the Rice University and Texas Medical Center campuses, as well as in several commercial developments. The impact of these systems on stormwater runoff is addressed in the following study. Monitoring studies that have attempted to quantify the individual performance of these features in terms of both water quality and quantity have indicated that most LID features improve stormwater runoff overall (Davis, et al., 2009; Dietz, 2007; Ahiablame,

18 18 et al., 2012). However, the results depend on a number of external variables such as climate conditions, seasonality, degree of maintenance, and also the impacts are highly site-specific (Davis, et al., 2009; Davis, et al., 2012; Dietz, 2007; Line, et al., 2008). Modeling-based studies have similarly sought to quantify the hydrologic performance of site-scale LID features, but there is a marked lack of consensus on the optimal modeling techniques to represent these features (Elliott & Trowsdale, 2007). Most studies that extend beyond the site scale have only looked at the hydrologic impacts of LID features in small watersheds, on the order of a few square miles in area, using Curve Number (Damodaram, et al., 2010; Holman-Dodds, et al., 2003) or lumped modeling (Abi Aad, et al., 2010) techniques to represent LID features. Modeling research indicates that LID features have the potential to significantly reduce peak flows and volume, but the degree of reduction is highly variable and dependent upon regionally-specific factors such as soil infiltration, rainfall magnitude (Holman-Dodds, et al., 2003) and rainfall distribution (Abi Aad, et al., 2010). Possibly because of the uncertainties, few studies have attempted to evaluate the cumulative hydrologic impact that site-scale LID features have when distributed throughout a large watershed. This is a particularly important question in urban areas where flat topography, low-permeability soils, and high intensity rainfalls exacerbate flooding and drainage issues. To address these gaps in our understanding of LID feature performance, the following hydrologic modeling study considers watershed-wide distributions of urbanscale LID features and intense rainfall events characteristic of the Gulf Coast where LID implementation has been slower to evolve in comparison to other regions of the U.S. Although LID features are often utilized for water quality benefits and pollution load

19 19 reduction goals (Dietz, 2007), the focus of this investigation is constrained to only the hydrologic impacts (i.e. peak flow and volume reduction) due to the importance of flood control in this region Hydrologic Modeling The large areas and complex networks of watershed drainage often make field data collection expensive and labor-intensive. Hydrologic models are necessary to simulate processes such as overland runoff generation, water infiltration, storage, and flow routing. These models provide insight into the data collection, organization, representation, and analysis of actual watersheds. Hydrologic models are split into two categories based on the methodology that is used to represent a watershed area: lumped models and distributed models Lumped Hydrologic Models The lumped methodology assumes that all hydrologic processes occur at one spatial point in a subwatershed, and requires one set of uniform or averaged parameters for a single area. This approximation allows for analysis with relatively limited data measurements. It has been popularized by the U.S. Army Corps of Engineers HEC-HMS model, (originally released in 1973 as HEC-1) and is considered to be the most versatile and most universally accepted model for watershed analysis (Bedient, et al., 2013). One of the limitations of the lumped model, however, is the computational power required to represent a high resolution distribution of spatial parameters over a large area,

20 20 such as what would be associated with watershed-scale implementation of rain gardens and green roofs. Other lumped models, such as the Stormwater Management Model (SWMM) (Huber & Dickinson, 1988), are frequently used to represent detailed site-scale drainage and individual LID features (Abi Aad, et al., 2010; Elliott & Trowsdale, 2007). These analyses, however, are generally limited to representation of small areas (1-10 square miles), such as is common for a subwatershed or for neighborhood-scale drainage areas. This is due to the level of detail of data available and computational power required to increase the spatial extent (Bedient, et al., 2013; Vieux, 2004) Distributed Hydrologic Models A high-resolution alternative to the lumped-parameter method for watershed analysis is a physics-based distributed parameter method. Over the past decade, technological advancements in data measurements have facilitated the collection of detailed datasets of land topography, land use, land cover, soil distributions, and spatial variability of rainfall. The availability of this data as well as advancements in digital Geographic Information Systems (GIS) allow a higher level of spatial and temporal accuracy necessary to analyze watershed drainage processes. Despite the popularity of lumped models, distributed models are becoming more common in hydrologic assessments because they are better suited to fully utilize data advancements (Vieux & Bedient, 1998). Distributed models generally represent watersheds in a gridded-cell format in which individual grid cells represent areas as small as a few square feet to a few acres. Hydrologic processes are accounted for and runoff is routed between nodes along the

21 21 boundary of each cell. Physics-based distributed models utilize governing equations that account for conservation of mass, momentum, and energy in order to account for flow across a network of finite elements. The network is solved simultaneously at each time step in the analysis. Each cell element has a specific set of associated parameters to account for friction, flow direction, infiltration or saturation excess, and flow routing. This allows a higher analysis of resolution for watershed response: outputs are generated at any location within the watershed. The spatial resolution and variability of data allow more subtle changes in the landscape to be taken into account and evaluated over a large area without negatively affecting computing power. Although studies have shown that lumped models accurately represent large watersheds for even relatively low resolution data (Bedient, et al., 2003), the spatial variability in parameters associated with distributed models is necessary to account for the hydrologic performance of small-scale landscape changes, as is the case for LID features. This method is therefore more useful for simulating watershed-scale LID implementation and will be used in the following analyses. An additional benefit of distributed models is their ability to better utilize radarrainfall inputs. Typical lumped models use historical storm events from weighted rain gauges or uniform design rainfalls. While lumped models can use radar-rainfall inputs, each subarea receives a single hyetograph input for the whole basin. Distributed models, however, can effectively utilize high resolution data gathered by regional radars, such as NEXRAD, which vary incrementally both in time and space. Not only is radar a more accurate representation of historic storm events and less prone to error than rain gauges, but it also accounts for subtle rainfall patterns and localized regions of intense rainfall

22 22 that significantly affect watershed response (Wilson & Brandes, 1979; Vieux & Bedient, 1998; Vieux, 2004). Examples of common and well-studied distributed hydrologic models include the Danish Hydraulic Institute (DHI) MIKE models and the GIS-based model Vflo (DHI, 2011; Vieux, Inc., 2002). The Vflo model has been designed to be easily coupled with ArcGIS and geospatial datasets when available and has been used frequently in applications for shallow-sloping developed watersheds (Fang, et al., 2010; Teague, et al., 2013; Doubleday, et al., 2013; Duncan, 2011). For these reasons, the distributed hydrologic model, Vflo, is used to represent LID distributions in a watershed in the Gulf Coast Region for this study Summary of Objectives The following hydrologic modeling study focuses on two significant gaps in our understanding of Low Impact Development infrastructure: the lack of a simple methodology for evaluating LID at the urban watershed scale, and the unknown cumulative performance of LID features under urban and high-intensity rainfall conditions. The purpose of this investigation is to therefore pursue the following objectives: 1. Develop and test a distributed model method for representing commonly used site-scale infiltration-based LID retrofits (rain gardens and green roofs) across a large urban watershed.

23 23 2. Evaluate the cumulative hydrologic response of a watershed to rain garden and green roof retrofits under various magnitude, high intensity rainfalls. 3. Investigate and compare factors driving the ability for LID features to cumulatively alter peak discharge, runoff volume, and timing at the watershed outlet through an analysis of the following variables: a. LID feature infiltration and surface roughness b. Spatial distribution of rainfall c. LID feature distributions and relative locations within a drainage basin Using the Vflo software framework, a fully-distributed high-resolution model of White Oak Bayou Watershed was created, calibrated, and manipulated to evaluate three theoretical watershed-wide rain garden and green roof implementation scenarios in Houston, Texas. The following sections will describe an application of the Vflo model to White Oak Bayou, the development of methodology for incorporating small-scale LID features, and the resulting modeled hydrologic performance of these LID features along with a sensitivity analysis to describe a possible range of performance variations.

24 Chapter 2 Model Background and Application The following investigation utilizes the hydrologic modeling software, Vflo, created and distributed by Vieux and Associates, Inc. to develop and calibrate a fully distributed model of a highly developed urban watershed in a high intensity rainfall region. Vflo was used to represent landscape features and to simulate the hydrologic response of the watershed to specified rainfall conditions. A base model of the current watershed development was built and calibrated to three historic storm events General Description of the Vflo Model Vflo is a fully-distributed physics-based model that utilizes geospatial data and distributed rainfall inputs to simulate stormwater runoff in a watershed. Digital Elevation Models (DEM) inform slope and flow direction, while raster datasets for soil characteristics and land cover are used to determine infiltration and roughness values (Vieux, 2004). 24

25 25 Figure Vflo Geospatial Data Inputs. Each cell is capable of receiving flow inputs from eight different directions in the horizontal plane and can be classified as one of the following cell types: base, reservoir, overland, or channel. Base cells do not have the ability to infiltrate rainfall and purely

26 26 translate any inflow hydrographs downstream. A cell can also be categorized as a reservoir in which the cell functions as a detention basin based on input storagedischarge or stage-storage relationships. Overland cells do infiltrate rainfall and route runoff using the Kinematic Wave Analogy (KWA) for overland flow. The KWA operates under normal flow assumptions, enforcing water surface and friction slopes parallel to the land surface, and the assumption of no backwater dynamics. The KWA in the Vflo model solves for flow depth using the continuity equation: y 0 t + q = i f, x Equation 2-1 Kinematic Wave Analogy Continuity Equation. where y 0 is mean depth of flow, q is flow rate per unit width, i is rainfall rate, and f is infiltration rate. Flow rate is determined using a form of the Manning s Equation under uniform flow conditions (friction slope = channel slope) for flow per unit width over a shallow wide plane: q = k n y5/3 S 0 1/2, Equation 2-2 Manning s Equation for Overland Flow. where k is a unit-dependent dimensionless constant (k = 1.49 in U.S. units), n is the dimensionless Manning s roughness coefficient representing all factors causing

27 27 momentum extraction from overland flow, y is depth of flow, and S 0 is the bed slope. Values of n are empirically derived and are generally larger for shallow overland flow than for channel flow. The driving factor determining depth of runoff in the continuity equation is the rainfall excess generated from the difference between the rainfall rate and the rate of infiltration in a single cell when rainfall intensity is greater than the theoretical infiltration rate. Infiltration rate is calculated in the Vflo model using the Green and Ampt Equation: f = K s (1 M dψ F ), Equation 2-3 Green and Ampt Infiltration Equation. where K s is saturated hydraulic conductivity, M d is initial moisture deficit, ψ is wetting front suction head, and F is depth of infiltration. Green and Ampt soil parameters have been determined for a variety of soil classifications across the U.S. based on laboratory experiments and regression techniques by Rawls et al. (1982). This equation has been proven to be successful at representing infiltration and runoff in both laboratory and in modeling set-ups (Vieux, 2004). Runoff calculated in Vflo is then routed through a drainage network composed of overland and channel elements. The solution of the KWA equations within a network of finite elements relies on an explicit solution proposed by Vieux et al. (1990) and later presented within the two-dimensional context of GIS (Vieux & Gauer, 1994). A

28 28 watershed is modeled as a multitude of connected overland and channel elements draining to one or many distributed outlets. Figure 2.1.2: Vflo Grid-cell System and Cell Components. (Figure adapted from Doubleday, et al., 2013). Channel cells can be set up as uniform trapezoidal cross sections or non-uniform geometric cross sections based on surveyed data or cut from high resolution DEMs. Channel cells route flow using either specified stage-discharge relationships, the kinematic wave analogy for channel flow, or Modified Puls routing. The Modified Puls method allows storage to be better represented within shallow-sloping channels than kinematic wave routing, which tends to over-predict discharge (Vieux, 2004). Where high-resolution elevation data is available to create detailed channel cross sections, the Modified Puls storage-discharge routing with the Manning s Equation for open channel flow is best utilized.

29 29 Q = k n R2/3 S f 1/2 A, Equation 2-4 Manning s Equation for Open Channel Flow. where n is channel roughness, R is hydraulic radius (cross sectional area/wetted perimeter), S f is friction slope (equivalent to channel slope under uniform flow conditions), and A is cross-sectional area of flow. Again, the Manning s channel roughness coefficient is empirically derived and typically equals for impervious lined channels and 0.04 for vegetated channels (HCFCD, 2010; Chow, 1959). The Vflo model with Modified Puls channel routing was used in this study to develop a watershed model of current and theoretical LID developments in a portion of the City of Houston known as White Oak Bayou Watershed. This area was chosen for analysis due to its highly developed nature, distributed land use patterns, flooding issues, and because there is a large amount of data collected and published on the watershed s hydrologic characteristics, such as landscape features and rain/stream gauge networks White Oak Bayou Study Area The wealth of geospatial and hydrologic data available for the White Oak Bayou Watershed in Houston, Texas (Figure 2.2.1) allows it to be well represented in the distributed Vflo modeling software. Because this watershed is also located in a large metropolitan area, is highly developed, encompasses a large residential area, and is highly flood prone, it is an ideal study location for a hydrologic analysis of urban-scale LID features.

30 30 Figure 2.2.1: Relative Location of the White Oak Bayou Watershed in Harris County, Texas. The City of Houston was founded in 1836 at the confluence of two main regional waterways: Buffalo Bayou and White Oak Bayou. Early development expanded outward from this point and into the two watersheds. White Oak Bayou runs 26 miles from its headwaters to the confluence with Buffalo Bayou near Houston s current central business district. Although originally a natural bayou, baseflow in White Oak Bayou is now primarily provided by wastewater treatment plant effluent, with rainfall runoff accounting for over half of the discharge during storm events (Rogers & DeFee II, 2005). White Oak Bayou runs parallel to a major transportation corridor, Highway 290, and perpendicular to the concentric growth rings of the City s outward development trend (Rogers & DeFee II, 2005).

31 31 White Oak Bayou lies within Harris County, the most populous county in Texas, and is about 45 miles inland from the Gulf of Mexico. The watershed has an average slope of 0.1%, or 6.5 feet per mile (Rogers & DeFee II, 2005), and ranges in elevation from 159 feet mean sea level (ft msl) in its northwest corner to 40 ft msl at the outlet (Figure 2.2.2). Figure White Oak Bayou Watershed and Elevation Map. Soils in this area are typically poorly-draining loams with high clay content (Rogers & DeFee II, 2005). Although there are several regional detention ponds and small lakes, there are no major standing bodies of water in White Oak Bayou. A study by Liu and Wang (2008) mapped the locations of 18 large (greater than 12 acres in size) detention basins in the White Oak Bayou watershed, but little data exists on the storage-

32 32 discharge performance or other hydrologic attributes of these detention basins due to a lack of common oversight and site plan records for detention ponds (Emerson, et al., 2005) 1. Due to its proximity to the Gulf Coast, weather in this sub-tropical region is characterized by frequent high intensity rainfall events, intermittent drought periods, and seasonal hurricanes. The average annual rainfall for Harris County is 48 inches and follows an SCS Type III Rainfall Distribution Curve (Appendix 4). The 100-year (1% probability) rainfall event is equivalent to approximately 13 inches over 24 hours, while the 10-year and 2-year 24-hour events total 7.6 inches and 4.4 inches respectively (Table 2.2.1). Table Major Return Period Rainfalls for Northwest Harris County, Texas. Return Period (Occurrence Frequency) Rainfall Totals (in) Duration 2 YR (50%) 5 YR 10 YR 25 YR 100 YR (20%) (10%) (4%) (1%) 1 HR HR HR HR HR These return period rainfall intensities represent some of the highest in the U.S., exceeded only directly along the seaward Gulf Coast perimeter (Hershfield, 1961). When 1 Note: these detention ponds are also not included in the current HCFCD floodplain analysis for White Oak Bayou.

33 33 combined with the region s flat topography and relatively impermeable soils, high intensity rainfall is the basis for frequent flood issues in this traditionally swamp-like basin. Between 1948 and 2000, the percentage of developed land and total impervious area in the White Oak Bayou watershed increased by an order of magnitude. These changes have led to a much higher volume and faster time of concentration of runoff in the bayou (Rogers & DeFee II, 2005). An earlier study using Vflo showed that wetland losses between 1980 and 2008 in the Cole Creek subwatershed of White Oak Bayou caused a 15% increase in the 2- to 10-year observed flood peak flows (Duncan, 2011). To address the increased storage capacity required for the bayous to sufficiently drain surrounding areas and prevent flooding, the U.S. Army Corps of Engineers (USACE) undertook an extensive federally-funded flood damage control project in the 1970 s. The project re-channelized the lower 10.7 miles of White Oak Bayou and one of its major tributaries, Brickhouse Gully. These channels were reshaped into uniform trapezoidal cross sections and lined in concrete to decrease roughness resistance to flow and improve bank stability (HCFCD, 2012). Since the completion of these flood control alterations on White Oak Bayou in 1975, the City of Houston has almost doubled in size. As of 2012, White Oak Bayou Watershed is about 90% developed and covers an area of nearly 111 square miles. The watershed contains approximately 146 miles of open streams, including its major tributaries Little White Oak Bayou, Brickhouse Gully, and Cole Creek.

34 Flood Control in White Oak Bayou Despite the fact that one of the highest elevation residential areas in the City of Houston (known as the The Heights ) lies within White Oak Bayou Watershed, residential areas in this watershed routinely see some of the worst flooding associated with large-to-moderate storm events. Studies have shown that flooding has not been significantly reduced since the adoption of new floodplain management plans by Harris County Flood Control District (HCFCD) in the 1980 s (Rogers & DeFee II, 2005; HCFCD). Notably, storms in March 1992, June 2001 (Tropical Storm Allison), June 2008 (Hurricane Ike), and April 2009 caused significant commercial and residential flood damages around the downstream lined portion of White Oak Bayou. Currently the projected 500- and 100-year floodplains extend well beyond the channel confines in most of the watershed. It is evident that this watershed is highly flood prone from the fact that even the 10-year floodplain (Figure 2.2.3) shows significant inundation and flooded structures along tributary streams and the upper portion of White Oak Bayou. HCFCD has reported that approximately 5400 homes lie within this 10-year floodplain (HCFCD, 2014).

35 35 Figure White Oak Bayou 10-year Floodplain Showing Inundated Structures (Floodplain extent generated from HCFCD 2010 HEC-RAS Model E ; watershed boundary delineated by HCFCD). To address this more recent flooding issue, a large-scale combined effort between the USACE and HCFCD has been underway since 1998 to construct six additional stormwater detention basins for a total storage capacity of 3380 acre-feet, and to add 15.4 miles of channel improvements to the bayou. These improvements are projected to cause water surface reductions of up to 1.5 feet in the 100-year floodplain (HCFCD, 2014). In addition to these improvements, channel restoration is beginning to be considered along

36 36 with flood risk reduction efforts (HCFCD, 2012), but would require additional watershed storage to account for reduced conveyance capacity. Large-scale regional detention is a commonly used method for controlling and reducing peak flow rates during large storm events (Semadeni-Davies, 2005), but the high degree of development in White Oak Bayou limits the area available for future planning of these land-intensive controls. The concept of urban Low Impact Development (LID), however, utilizes enhanced infiltration or storage in small distributed areas to control water quantity. The potential for this method to control the high intensity storms observed in the Gulf Coast or to have a significant cumulative impact on downstream hydrology when implemented across a large watershed is not understood, but may be an option for reducing flood discharges in White Oak Bayou. Although the distributed nature and small size of urbanscale LID features may make it difficult for this style of LID development to significantly reduce impacts during a 100-year event, it may be beneficial in controlling flood discharges associated with storms at or below the 10-year level. The following thesis attempts to evaluate the feasibility of a distributed LID development scenario from a hydrologic perspective through simulations of LID features in a watershed under various high intensity rainfall conditions.

37 Base Model Development Model Application A fully-distributed hydrologic model of White Oak Bayou Watershed was created and calibrated using the desktop software Vflo. High-resolution datasets of watershed parameters were gathered and processed in ArcGIS to the correct extent and format for the model. A full list of data sources and resolutions are listed in Table Table Vflo Input Data and Sources for Development of White Oak Bayou Base Model. Dataset Vflo Input Parameters Source Resolution DEM Elevation, slope, flow direction, channel cross ft sections HGAC Land Cover Manning s overland roughness ft NOAA Imperviousness Imperviousness ft NLCD Soils hydraulic conductivity, capillary pressure, effective porosity, soil depth 2003 NRCS 150 ft The Vflo AutoBOP feature allows input of GIS-based raster datasets for elevation, surface, and soil parameters. LiDAR data from 2008 was processed to determine the location of flow paths and channels within the watershed. The watershed boundary and locations of channels were enforced by overlaid channel shapefiles to ensure existing channels were accurately delineated by the model. The cross sectional geometry of each channel cell within the model was also determined from 15-ftresolution LiDAR data and verified at several locations with surveyed cross section data.

38 38 Overland flow characteristics such as infiltration rate, roughness, and imperviousness were determined from maps of soil and land cover data. Soil parameter data was linked to a geospatial soil distribution map to determine depth, texture (NRCS, 2012), effective porosity, wetted front capillary pressure, and hydraulic conductivity (NCRS, 2003; Rawls, et al., 1983) (Table 2.3.2). Table Green and Ampt Soil Parameters for White Oak Bayou Soils. Texture Classification Horizon A Depth (in) Effective Porosity Wetted Front Capillary Pressure (in) Hydraulic Conductivity (in/hr) Sand Voss Loamy fine sand Boy, Kennedy Sandy loam Loam Silty clay loam Clay loam Clay Aris, Atasco, Edna, Hockley, Katy, Wockley, Aldine, Bissonnet Addicks, Clodine, Gessner, Hatliff, Nahatche, Ozan Verland Bernard Beaumont, Ijam, Lake Charles, Vamont Manning s roughness values are lumped parameters accounting for all factors which cause a frictional resistance to flow, such as vegetation, bed forms, flow path or

39 39 channel sinuosity, debris, bed friction from sediment or bed material, and flow obstructions (Chow, 1959). These values are typically higher for overland flow than channel flow due to the influence of flow depth relative to the height of frictional factors (McCuen, 2005). Roughness values in the model were initially assigned by land cover classification based on values calculated for the adjacent Greens Bayou Watershed in Harris County, TX (Kalyanapu, et al., 2009). These values are listed in Table Table Overland Roughness Parameters Based on Land Cover Classifications adapted from Kalyanapu et al NLCD ID Land Cover Classification Roughness 24 High Intensity Developed Medium Intensity Developed Low Intensity Developed Developed Open Space Cultivated Bare Land Grassland Deciduous Forest Evergreen Forest Mixed Forest Scrub/Shrub Woody Wetlands Emergent Herbaceous Wetland Open Water Because a significant portion of White Oak Bayou has been lined in concrete, lined segments were identified in the model and channel roughness for those cells was manually set to a uniform value of This pre-calibration value was chosen because it is the Manning s roughness value associated with concrete cover used by the HCFCD in the agency s hydraulic modeling and floodplain analysis (HCFCD, 2010). All other

40 40 channel cells were assigned roughness values of 0.04, which is the HCFCD Manning s roughness value associated with vegetated channels. Evapotranspiration was not included in the model due to a lack of high-resolution data for the Houston area and the high uncertainty associated with evapotranspiration values interpolated in spatially distributed models (Phillips & Marks, 1996). Evapotranspiration will also have little effect on runoff over the duration of single rainfall events, which is what this investigation focuses on. The resolution of the grid cells within the model was set at 350 feet to minimize the model run time and error associated with low slopes at a high resolution. The watershed itself contains a total of 25,300 cells. Of these, 3000 represent channel cross sections and the rest represent overland flow Sensitivity Analysis Prior to calibration, a model parameter sensitivity analysis was performed on the initial White Oak Bayou data inputs to determine which overland flow parameters were most sensitive to peak, volume, and timing at the watershed outlet. A 100-year/24-hour spatially-uniform design rainfall was used for this analysis to constrain the effects of spatial rainfall variability, to ensure runoff generation, and to aid in the initial calibration for an event of similar magnitude. Overland and channel cell values for roughness, hydraulic conductivity, and imperviousness across the entire watershed were uniformly multiplied by calibration factors ranging from 0.01 to 5 2, and the resulting peak, volume, 2 The model framework prevents imperviousness values greater than 1 (100%).

41 41 and timing of flow at a location near the outlet gauge ( ) were recorded and compared. Additionally, initial soil saturation was varied from 0 to 100%. A sensitivity analysis on the Green and Ampt infiltration parameters (soil depth, hydraulic conductivity, and wetting front suction head) showed that hydraulic conductivity consistently drives changes in volume and timing, while variations in wetting front suction head and soil depth have little effect (Appendix 1). Hydraulic conductivity was thus used as a proxy for infiltration rate and compared with the other model surface parameters to determine its relative influence on hydrologic response (Figure 2.3.1). As expected from the inverse relationship between infiltration and discharge (Equation 2-1), increases in hydraulic conductivity decreased peak discharge indirectly through reducing the volume of runoff at the outlet. Because increases in initial saturation and imperviousness reflect a reduction in infiltration capacity, variations in these two parameters followed the opposite trend. The timing of peak discharge, however, is directly proportional to roughness, as roughness creates a frictional resistance to flow, thus slowing the flow.

42 Increase in Timing (hrs) % Increase in Peak % Increase in Volume 42 Sensitivity Analysis - Volume 15% 5% -5% (a) -15% Calibration Factor Sensitivity Analysis - Peak (b) 130% 80% 30% -20% -70% Calibration Factor 6 Sensitivity Analysis - Peak Timing Calibration Factor Overland Roughness Channel Roughness Imperviousness Hydraulic conductivity Initial Saturation (c) Figure Base Model Sensitivity Analysis Results.

43 43 Plot (a) in Figure displays the sensitivity of volume observed at the outlet in relation to infiltration and roughness parameters in the model. Increases were calculated in relation to initial parameter values (corresponding to a calibration factor of 1). Hydraulic conductivity and initial saturation have the largest degree of influence on increases in volume, while reduction in imperviousness has the most significant impact on decreasing volume. Both overland and channel roughness have very little influence on volume. This is due to the fact that roughness can only serve to reduce volume by slowing the conveyance of water and allowing more time for runoff to infiltrate. Flow retardation due to roughness, however, has a much more significant impact on reduction in peak discharge as evident in Figure (b). Channel roughness has a greater degree of influence than overland roughness in this respect because the roughness acts over a greater hydraulic radius of flow in a channel. For the same reason, timing of peak discharge in the model is most sensitive to changes in channel roughness, since it is the Manning s Equation in the Modified Puls routing method which determines the timing and peak of discharge routed to the outlet. Based on these results, channel roughness, hydraulic conductivity, imperviousness, and initial saturation are most important to calibrate correctly, as the model is most sensitive to these parameters. As will be discussed in the next section, initial saturation was only included in the final stages of calibration for each historic event and will not be included in the theoretical simulations.

44 Model Calibration To verify that the parameters in the Vflo model of White Oak Bayou most accurately represent current hydrologic conditions, the model was calibrated to three recent large-to-moderate storm events and validated with a fourth smaller event. The size of these historic events was chosen to provide a range of storm intensities over which to calibrate the model. This was to ensure that the final model is able to accurately represent the watershed s hydrologic response to a range of rainfall patterns. Rainfall data for the four calibration events were taken from NEXRAD gauge-adjusted radar when available or from historic records of rain gauges. Rain gauge data was interpolated using an inverse distance weighting method across the watershed. A relatively dense rain gauge network jointly owned and operated by Harris County, the Texas Department of Transportation (TXDOT), and the City of Houston, produced time-series data for the three smaller events. Historic stream flow data from existing USGS stream gauges within the watershed were used to calibrate to each storm. Five of these gauges record time-series discharge data, while the gauge near the outlet only collects stage data because it is within the short tidally-influenced downstream zone of White Oak Bayou and therefore does not have an established stage-discharge relationship. Discharge for this gauge was thus approximated from observed stage data using a rating curve calculated in the TSARP HEC-RAS model for the 100-year storm at this location. Figure shows the IDs and locations of each rain and stream gauge. Table describes the historical rainfall events used for calibration. Baseflow was added at

45 45 each of the gauge locations based on USGS observations to match initial dry-weather stages.

46 46 Figure Rain and Stream Gauge Networks and Subcatchment areas in and around White Oak Bayou Watershed in Houston, TX. Table Historical Rainfall Events Used for Calibration of the White Oak Bayou Base Model. Event Duration Total average Model input method rainfall over White Oak Bayou Hurricane Ike 48 hrs 11.2 in Distributed radar file (2008) July 11-13, hrs 5.3 in Interpolated rain gauges 24 hrs 4 in Interpolated rain gauges September 20-21, 2013 October 30-31, hrs 3.3 in Interpolated rain gauges Once a model domain had been created within the Vflo framework, roughness and infiltration parameters were calibrated using rain and stream gauge data from three historic storm events in White Oak Bayou. The final calibrated model was then verified against a fourth smaller storm and the Mean Absolute Percent Error (MAPE) differences in peak flow and runoff volume were calculated for each gauge and each storm event Hurricane Ike (11.2 in/48 hrs) On September 13, 2008, the second most destructive hurricane in Texas history made landfall on Galveston Island (Bedient & Sebastian, 2012). Over a two-day period, Hurricane Ike and its resulting tropical depression dumped a maximum of 13 inches of rain inland over White Oak Bayou. The watershed received an average total rainfall of

47 inches in two waves: once on the morning of September 13 th and again in the early hours of September 14 th. The two-wave nature of the rainfall produced a double-peaked stream flow response at all six stream gauge locations. This storm was chosen for initial calibration because total rainfall was approximately equivalent to a 100-year storm event for this area, and the watershed was far enough inland that the effects from storm surge were minimal. A gauge-adjusted radar file of Hurricane Ike inland rainfall was processed and provided by Vieux Inc. and imported into Vflo as a temporally and spatially distributed precipitation dataset (Figure 2.3.3). Figure Spatially-distributed Rainfall Totals (inches) over White Oak Bayou during Hurricane Ike.

48 48 USGS-observed data at the five gauge locations during Hurricane Ike were compiled and loaded into the model to compare modeled and observed hydrographs. Beginning downstream and working upstream, overland and channel parameters within the catchment area of each gauge were adjusted to match the modeled and observed volume, peak, and timing of flow at each location. Roughness, hydraulic conductivity, and wetting front were changed by uniform factors at each of the six catchments upstream of a stream gauge. Channel roughness and overland roughness were calibrated independently. The double-peaked nature of this event made it especially difficult to calibrate, so only the larger of the two peaks was used for comparison at each gauge. Final calibration errors averaged across all six gauges for Hurricane Ike show peaks and volume matching within 12% of observed. Discharge hydrograph outputs from the Vflo model comparing modeled and observed flows at the two gauges along White Oak Bayou nearest the outlet ( and ) are shown in Figure

49 49 Figure Calibration Results for Hurricane Ike at Lower White Oak Bayou ( ) and the Outlet ( ) Gauges. Peak discharge during the second peak at both gauges is slightly overestimated by 8 to 24%. Volume is underestimated by 1 to 36%, with the large error likely due to an approximated rating curve at the outlet.

50 July 2012 Storm (5.3 in/72 hrs) Between July 11 th and 13 th, 2012, an average of 5.3 inches of rain fell over White Oak Bayou. Although intermittent rainfall lasted a total of seven days, only the most intense period of rainfall, which fell within a 72-hour window between July 11 th and 13 th, was used for the calibration. The nature of the rainfall has a central peak, with several small forerunners and late waves of rain. Historical time-series data collected from 40 independent rain gauges in one-hour increments were input into the Vflo interpolated gauge precipitation model based on the geospatial location of each gauge. The model then interpolated rainfall between the gauges using an inverse distance weighting method. Beginning with the calibrated overland parameters from Hurricane Ike as a base, the July 2012 storm event was run and hydrographs were compared at the six stream gauge locations. Calibration factors were again adjusted to find a best fit in terms of peak, volume, and timing. Initial saturation was included and calibrated to reflect the antecedent rainfall conditions of this storm. Final calibration results show that the average modeled peak flow was less than 20% different than observed and modeled runoff volume had an average error of 12%. Calibration results for discharge at the two gauges nearest the downstream end of the watershed are shown in Figure

51 51 Figure Calibration Results for July 2012 Storm at Lower White Oak Bayou ( ) and the Outlet ( ) Gauges. Peak discharge during the central peak at both gauges is overestimated by 13-14%. Volume is over-predicted by 26%.

52 September 2013 Storm (4 in/24 hrs) A third smaller historical storm event was run in the White Oak Bayou model for a final calibration. This storm occurred from September 20 th to 21 st, It is a singlepeaked event with a total rainfall of about four inches in 24 hours. The same interpolated gauge method as the July 2012 storm was used to distribute publically available Harris County rain gauge data across the model domain. Final calibration results show that peak flow matched within 16% of observed data across the watershed. Results for the two gauges closest to the downstream end of the watershed are shown in Figure Peak discharge is underestimated at the outlet gauge by 10% and overestimated at the Lower White Oak Bayou gauge. Volume at both gauges is modeled within 20% of observed.

53 53 Figure Calibration Results for September 2013 Storm at Lower White Oak Bayou ( ) and the Outlet ( ) Gauges. Final calibration factors can be found in Appendix 2. A small 3.3 in/36 hrs singlepeaked storm on October 30 th to 31 st, 2013 was used to validate the final calibration. Volume for this storm was modeled on average within 24% of observed, while peak flow

54 54 matched within 17%. In general for the four storm events at all six gauges, the White Oak Bayou Vflo average prediction error was within 20% of observed. Full calibration hydrograph results at all other gauges can be found in Appendix 6. Peak and volume error between modeled and observed hydrographs at each stream gauge are listed in Table Table Summary of Errors in Volume and Peak Flow for Final Calibrated Base Model at all Stream Gauges for Observed Data During Four Storm Events in White Oak Bayou. Stream Gauge Difference between modeled and observed data 3 Hurricane July September October Ike Gauge MAPE 4 Upper White Oak Lower White Oak Outlet Little White Oak Main Channel Gauges Volume -20.4% -1.7% 20.3% 20.2% 15.7% Peak 0.5% 16.5% 23.6% -19.2% 15% Flow Volume -1.4% 26.2% 3.7% ND % Peak 23.4% 36.6% -10% ND 23.3% Flow Volume -35.7% 0.8% -18.4% -24.4% 19.8% Peak 8.2% 13.7% 12.7% -23.5% 14.5% Flow Tributary Gauges Volume -2.6% 23.7% -3.1% 21.2% 12.7% Peak 124.6% % -22% 0.7% 14.3% 3 Note that a positive percentage indicates an over-estimation, while a negative percentage indicates an underestimation. 4 Mean Absolute Percentage Error (MPE): absolute value average of all errors across individual gauges or storm events 5 No data (ND) was recorded at stream gauge during the October 2013 storm, so it was omitted from the final validation and overall average percent-difference calculations.

55 55 Cole Creek Brickhouse Gully Flow Volume 6.1% 0.2% 6% 12.7% 6.3% Peak 21.6% -4.4% 8.4% -34.8% 17.3% Flow Volume 5.1% -20.9% 23.2% 38.4% 21.9% Peak Flow 4.7% -24.4% 18.4% -7.2% 13.7% Storm MAPE Volume 11.9% 12.3% 12.5% 23.4% Peak Flow 11.7% 19.3% 15.9% 17.1% Because there is a relatively low average range of error associated with the final model of White Oak Bayou when calibrated to relatively recent storm events, it can be considered an accurate predictor for future or hypothetical events. This base model will now be used to build a series of theoretical development scenarios considering watershed-scale implementation of LID retrofits. 6 A gauge-reporting error was assumed at the stream gauge on Little White Oak Bayou ( ) during Hurricane Ike. After inspection of the observed hydrograph shape, this anomaly was assumed to be due to a malfunction of the stage-recording device, causing the measured peak flow to be much lower and longer sustained than what should have been observed. This value was therefore omitted from the overall average difference calculations for Hurricane Ike and the Little White Oak stream gauge.

56 Chapter 3 LID Modeling Methodology The fully calibrated model of White Oak Bayou was amended to include landscape changes associated with three watershed-wide LID implementation scenarios: residential-scale rain gardens, commercial- and public-scale green roofs, and a combination of both features. Due to the small size of the LID features evaluated relative to the size of the model, a detailed set of assumptions were made to develop a framework for representing these features in a distributed model LID Model Development One of the challenges in evaluating the impact of distributed LID features was developing a methodology for representing LID within the existing model framework. Although the Vflo model is useful for accurately representing high resolution overland flow in a large area, it was not specifically developed to evaluate LID features. Previous studies have used Vflo to simulate wetlands (Duncan, 2011) and large low-impact flood 56

57 57 control features such as riparian corridors and conserved forested areas (Doubleday, et al., 2013), but as of yet no studies have represented retrofit-scale LID features, such as rain gardens and green roofs, in a distributed model. These features are much smaller in scale and cannot be represented as individual grid cells when evaluating watershed-scale processes due to their size. Instead, individual grid cells must be treated as pseudolumped models containing smaller LID features. Several different trials of LID features and associated assumptions were tested before two features were selected: residential-scale rain gardens and commercial/publicsector-scale green roofs (see Appendix 7 for more information). Both features have a different driving process affecting runoff peak, volume, and timing. Rain gardens are designed to maximize infiltration storage, while green roofs function to disconnect impervious cover and delay runoff. For modeling purposes, these features were uniformly assigned characteristic parameters based on existing LID feature designs, which will be detailed in the following sections LID Feature Modeling Assumptions Small-scale features within the model are represented using a cell-by-cell areaweighting method. Infiltration and overland roughness parameters of each cell were altered based on the total surface area of LID changes within the cell. For example, the general mathematical relationship used to calculate a new cell parameter, x, for a LID cell is:

58 58 For x 0 < x LID, x = (A T A LID )x 0 A LID x LID A T ; For x 0 x LID, x = x 0 ; Equation 3-1 General Weighted Parameters Cell-Averaging Equation where A T is total cell area ( ft 2 ), A LID is total surface area of LID features within a cell, x 0 is the original cell parameter value, and x LID is the parameter value for an individual LID feature. Cells with initial values higher than the associated LID parameter (or lower for imperviousness) were left unchanged so as to prevent error associated with an unintentional decrease in hydrologic performance. This formulation was used to recalculate overland roughness, total imperviousness, and the Green and Ampt infiltration parameters: hydraulic conductivity, wetting front suction head, effective porosity, and soil depth for each LID cell. LID cells are defined based on land use in the White Oak Bayou watershed. A lack of zoning restrictions in the City of Houston allowed theoretical distributions of LID features to be grouped based on land use. Area associated with residential, commercial, and public (i.e. government, medical, and educational) land uses were associated with LID features in the model based on current locations of LID feature sites in Houston and general observed associations between these land uses and specific LID features across the U.S. Under the assumption that a combination of features would have the most impact, three different LID implementation scenarios were evaluated to determine the relative

59 59 contribution of rain gardens and green roofs to reductions in peak, volume, and timing of runoff in the watershed: A. Rain gardens in all residential areas B. Green roofs in all commercial and public areas C. Scenarios A and B combined Modeling Rain Gardens as Residential LID Features Although relatively few retrofit LID projects currently exist in the White Oak Bayou watershed, a highly publicized LID demonstration project has been recently completed by the City of Houston in the Cottage Grove residential neighborhood in the southern half of the watershed. The project consists of eight infiltration-based rain garden features, each draining roughly an acre of land, located along two blocks of a residential street (Figure 3.1.1). Figure Cottage Grove Rain Gardens Post-construction. (Photos taken ).

60 60 Because of the project s location and City-approved design, it is used as a basis for a characteristic rain garden in the White Oak Bayou model using design parameters listed in Table (personal communication, Jones & Carter, Inc.). Because the features are designed with an underdrain feeding to the municipal storm sewer system, no additional subsurface storage was included in the model. Table Rain Garden Design Parameters Parameter Value Drainage area 7 1 acre (43560 ft 2 ) Surface area ft 2 Pervious surface roughness 0.4 Depth of soil media 1.5 ft Hydraulic conductivity 1.18 in/hr Wetting front suction head 2.4 in Effective porosity To simulate a maximum implementation scenario, rain gardens are represented in the Vflo model of White Oak Bayou in every cell corresponding to residential land use (LID Scenario A). Excluding empty lots, residential areas represent a total of 35% of land use in White Oak Bayou, the largest of any single land use category in the watershed. The current location of the Cottage Grove rain gardens is the basis for assigning rain garden features to residential areas in the theoretical LID development model. Because the City of Houston has initiated the project as a pilot-scale study of LID performance in a residential area, it is assumed that there is strong potential for the project to be 7 Approximated from the average of eight individual rain garden sizes and drainage areas.

61 61 extrapolated in the future to other residential areas in the watershed. The modeled scenario represents maximum implementation. Each rain garden is assumed to occupy 150 ft 2 of land and drain an additional acre. Given a cell size of 2.8 acres, each residential cell therefore contains two rain gardens, or a total of 300 ft 2 of LID features. Surface roughness was adjusted using Equation 3-1. Infiltration parameters, however, were adjusted independently to reflect the ability for rain gardens to infiltrate runoff from impervious areas in addition to rainfall. Because the Vflo computational framework only accounts for infiltration from rainfall, even when rainfall intensity is less than infiltration rate, there was no straight-forward way to account for subgrid interconnectedness of LID features and impervious areas. Instead, rain gardens were treated as a disconnection of total impervious surface, thus allowing a portion of the impervious area within a cell to be converted to pervious (i.e. infiltrating) area. This concept is visually represented in Figure for any infiltration parameter, x. Figure (a) illustrates the assumed sub-cell overland flow dynamics in which runoff from an impervious area would drain to and infiltrate through the rain garden at a higher rate than surrounding pervious area. Figures (b) and (c) show the interpretation of this process as a spatially-averaged uniform infiltration in the Vflo model.

62 62 Impervious Pervious x 0 p 0 k LID x LID Rain garden (a) x LID k LID x 0 x LID (b) x (c)

63 63 Figure Visual Representation of Rain Garden Infiltration Modeling Technique. (Figure (a) represents actual flow dynamics in which infiltration parameters would be different for pervious, LID, and impervious areas; (b) represents a simplification of these dynamics whereby the disconnected impervious area is interpreted as a pervious area; (c) represents the final area-averaged cell parameters). Thus for rain garden cells, the mathematical determination of amended infiltration parameters, x, (hydraulic conductivity, wetting front suction head, effective porosity, and soil depth) based on both the initial parameter, x 0, and degree of imperviousness, p 0, is: For x 0 < x LID, x = (1 A LID A T Cp 0 ) x 0 + Cp 0 x LID + A LIDx LID A T ; For x 0 x LID, x = x 0 ; Equation Weighted Parameters Cell-Averaging Equation for Rain Garden Infiltration Parameters Accounting for Disconnection of Impervious Area. This formulation is a variation of Equation 3-1, where C is a connection factor representing the fraction of impervious area in a LID cell disconnected by, or draining to, LID features. For this analysis, C is uniformly set to 0.5 in all LID cells based on an assumption that LID features would disconnect and drain half of existing impervious surfaces within each 2.8-acre residential cell. An initial sensitivity analysis on the connection factor showed that increasing C from a value of zero to one (minimum and maximum disconnection) would correspond to about a ±10% change in peak discharge and ±7% change in volume of runoff at the watershed outlet. Thus the chosen factor of C = 0.5 is represents the average of no-connection and full-connection assumptions.

64 64 This methodology is an attempt to account for some sub-cell routing for a more accurate representation of changes in infiltration associated with LID retrofits. It should be noted, however, that in simplifying these flow dynamics to a uniform set of parameters for each LID cell, the effect of timing is not being considered. In reality, impervious-topervious surface routing will cause a delay in the timing of runoff. In the Vflo model of LID scenarios in White Oak Bayou, flow is routed across individual cells using a single straight-line path and time step. Resulting measured peak timing of discharge should therefore be considered a conservative estimate of the impact of LID features in the watershed, as it may under-predict timing delays. Each Cottage Grove rain garden has a designed surface storage capacity of 37.5 ft 3 before overflow occurs 8. Ideally, the abstraction function in the Vflo model is used to account for this surface storage. Once rainfall intensity exceeds the infiltration rate in a cell, an additional user-specified abstraction depth of rainfall must be met before runoff can be generated. However, the relatively low storage volume in each rain garden compared to the overall cell area is equivalent to a total cell abstraction depth of less than 0.01 inches. Surface storage is therefore not expected to have any measurable impact on volume or timing of discharge in the watershed. 8 This volume is based on the following design parameters: an overflow drain height of 12 in above surface media, a 0.75 vegetation ratio, and a 150 ft 2 surface area.

65 Modeling Green Roofs as Commercial and Public LID Features Green roofs are among the first and most widely adopted LID features to be retrofit into existing urban spaces in the Houston area. In addition to providing water management benefits, green roofs became popular in Houston early-on for their ability to improve insulation and reduce energy costs (Webb, 2012). Extensive green roofs consist of a shallow vegetated covering typically planted in soil trays two to six inches in depth. This style of green roof is the least expensive and easiest to retrofit onto existing structures when compared to intensive green roofs which require greater structural support and maintenance (Kohler, 2004; Carter & Keeler, 2008). Extensive green roofs can be found around the City of Houston on several government, medical, and educational buildings, including at the Rice University campus, the University of Texas School of Public Health, and Building 12 at NASA s Johnson Space Center (Figure 3.1.3). This style of green roof is not only a current popular LID retrofit in Houston, but is also projected to comprise the majority of new green roof projects in Texas (Volder & Dvorak, 2014).

66 66 Figure Extensive Green Roofs on the NASA Johnson Space Center and Rice University Campuses. Based on the observed association between green roofs and public buildings in Houston and for the purpose of evaluating a maximum LID implementation scenario, all cells in the White Oak Bayou model corresponding with public land use were modeled as containing an extensive green roof. Excluding empty lots, public land represents only 4% of area in the White Oak Bayou watershed. Because this number is so low, commercial land (6%) was added to the modeled green roof coverage scenario (LID Scenario B). Unfortunately, no design parameters for a specific extensive green roof in Houston were able to be obtained. Instead, a set of assumptions were made based on the nature of extensive green roofs and reasonable associated surface and infiltration

67 67 parameters (Table 3.1.2). Soil media was assumed to be a sandy loam with the same properties as the rain garden sandy loam media. These values are identical to those provided by Rawls (1983) except for a slightly larger effective porosity. Likewise, surface roughness was set at 0.4 based on the published overland flow roughness value associated with light underbrush (McCuen, 2005). Table Green Roof Modeling Parameters. Parameter Value Drainage area 9 0 ft 2 Public roof surface area ft 2 Commercial roof surface area ft 2 Pervious surface roughness 0.4 Depth of soil media 4 in Hydraulic conductivity 1.18 in/hr Wetting front suction head 2.4 in Effective porosity Based on an average parcel-to-building footprint ratio of 10:1 (determined in GIS from analysis of building footprint and parcel data), only one green roof was modeled in each public or commercial 2.8-acre ( ft 2 ) LID cell. Because no infiltration of upstream impervious runoff needed to be accounted for in the green roof scenario, a standard weighted average (Equation 3-1) was used to determine amended surface roughness and infiltration parameters, including imperviousness. 9 Green roofs are stand-alone LID features disconnected from any upstream runoff. They are only able to infiltrate rainfall. Drainage area is simply the surface area of the roof. 10 Green roof areas were determined from a GIS analysis of the average roof area of buildings within the public and commercial land use areas respectively. Vegetation was assumed to cover 100% of roof area.

68 68 To validate the design parameters chosen to represent rain gardens and green roofs in this analysis, theoretical rainfall volume reduction (rainfall retention) of the individual LID features was calculated and is presented in Figure These values were calculated using a simple one-dimensional spreadsheet model of Green and Ampt infiltration (Equation 2-3) and the SCS Type III distribution of 2- to 11-in rainfall. The green roof assumptions used in this study produce rainfall retentions of approximately 20 to 80% for 8- to 2-in rainfalls, which match well with measured values in literature (20 to 90% for 3- to 0.1-in rainfalls). Rainfall retention for rain gardens is consistently higher than that of green roofs due to the greater soil depth. With the addition of 3 inches of surface storage, rain gardens should individually be able to retain 100% of rainfall, except during the 100-year (11.2 in) event. This is relatively high compared to measured values reported in literature (48 to 97% for 7-to 0.1-in rainfalls), but also does not include any upstream runoff from impervious areas.

69 Rainfall Retention (Runoff Reduction) 69 Theoretical Volume Reduction of Individual LID Features 100% 80% 60% 40% 20% 0% hr Precipitation (in) Green Roofs Rain Gardens w/ 3-in Surface Storage Rain Gardens (infiltration) Figure Runoff Reductions Predicted for Individual Rain Garden and Green Roof Features as Modeled in White Oak Bayou. In summary, for Scenario A, 35% of roughly evenly-distributed cells in White Oak Bayou were amended to simulate the watershed-wide implementation of rain gardens. Scenario B simulates the presence of extensive green roofs in 10% of cells. Scenario C combines the usage of rain gardens and green roofs across the watershed. An evaluation of the hydrologic performance of these scenarios compared to the base current development of White Oak Bayou Watershed under various rainfall conditions will be discussed in the following chapter.

70 70 Chapter 4 Results and Discussion Three different LID scenarios of rain garden and green roof implementations were simulated in the White Oak Bayou watershed model to evaluate the maximum hydrologic impact of these infiltration-based LID features, which have the potential to be retrofit into an urban landscape. Although conservation-based approaches to low impact development have been shown to have a significant impact on discharge at the 100-year level, the smaller-scale nature of the rain garden and green roof urban LID features evaluated in this analysis are limited in size and infiltration storage capacity and are typically designed to address more frequent storms. Therefore the watershed response to higher frequency, but still flood-causing, rainfalls will be the focus of the following analyses. Hydrologic performance was analyzed under three different uniform design rainfall events corresponding to 10-year, 5-year, and 2-year frequency rainfalls.

71 71 Hydrologic performance is quantified as a non-dimensional percent reduction of peak discharge or volume using the following method: % Reduction = (X LID X Base ) X Base 100%, Equation 4-1 Percent Reduction in Peak and/or Volume of Discharge. where X LID represents the peak or volume of runoff measured in one of the LID scenarios, and X BASE is the peak or volume of runoff measured at the same specified gauge location in the base model, which was set up and calibrated in Chapter Although changes in peak timing generally showed no significant trend, when reported timing is quantified as the duration of the delay in minutes. One of the unique components of this study is the focus on cumulative watershedscale hydrologic impacts, as opposed to site-scale impacts which are more frequently analyzed, but may have less of a contribution to overall flood control. The cumulative impact of watershed-scale distributions of LID features is evaluated based on a comparison of the hydrologic response for a given LID scenario to base conditions at the outlet gauge ( ). Sensitivity of the modeled hydrologic performance to variations in LID feature parameters, rainfall distributions, and orientation of features is subsequently evaluated to better understand the factors driving the ability for LID features to control runoff at the watershed outlet.

72 % Decrease in Peak Discharge % Decrease in Volume LID Performance for Design Storms Using the Vflo SCS Rainfall Distribution module, spatially-uniform Type III distributions associated with the 10-year, 5-year, and 2-year design rainfalls for Harris County were run in the White Oak Bayou Base and LID Scenario models. Results show that LID performance, in terms of peak and volume reduction at the outlet, increases as the storm magnitude decreases (Figure 4.1.1). No significant delay of peak timing was observed at the outlet for any of the storms. This indicates that peak discharge reduction was driven primarily by volume reduction from infiltration and not flow attenuation. Reduction in Peak Discharge at White Oak Bayou Outlet compared to Base Model 20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% A B C LID Scenario 10 YR 5 YR 2 YR Reduction in Volume of Runoff at White Oak Bayou Outlet compared to Base Model 20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% A B C LID Scenario 10 YR 5 YR 2 YR Figure Cumulative Hydrologic Performance of LID Scenarios A-C for 10- Year to 2-Year Design Storms Compared at the Watershed Outlet ( ).

73 Discharge (cfs) Rainfall Intensity (in/hr) Discharge (cfs) Rainfall Intenisty (in/hr) 73 For all three events, the temporal distribution of rainfall (SCS Type III) and theoretical infiltration rates are constant. Therefore as the magnitude of rainfall increases, the difference between rainfall intensity and infiltration rate (which produces runoff) increases, as does the duration over which rainfall intensity exceeds infiltration rate, thus the proportion of runoff infiltrated by LID features decreases with increasing rainfall intensity. This is colloquially described as a wash-out effect. This effect is again seen in the response of LID Scenario C to the 100-year design storm (13.2 inches in 24 hours). LID features caused only an 8% reduction in peak and 5% reduction in volume, which is about half as large as the reductions observed for the 10- to 2-year design storms (Figure 4.1.2). White Oak Bayou Outlet Response During 100YR/24HR Rainfall :00 12:00 12:00 Time BASE LID C Rainfall White Oak Bayou Outlet Response During 2YR/24HR Rainfall :00 12:00 12:00 Time BASE LID C Rainfall Figure Hydrographs of the Outlet Response under Base and LID Scenario C to the 100-year and 2-year Design Rainfalls.

74 74 The LID implementation scenario considering both rain gardens and green roofs (C) had the greatest cumulative hydrologic impact overall, causing a 13 to 18% reduction in peak discharge and an 8 to 10% reduction in volume. The full results for Scenario C, including the 100-year rainfall simulation, can be found in Table Table hour Design Storm Results for LID Scenario C. Design Storm Magnitude 100 YR/24 HR (13.2 ) 10 YR/24 HR (7.6 ) 5 YR/24 HR (6.1 ) 2 YR/24 HR (4.4 ) Peak Discharge (cfs) Runoff Volume (in) Timing of Peak Discharge (12:00 AM 11:59 PM) Base :45 PM Scenario C :36 PM Base :21 PM Scenario C :33 PM Base :45 PM Scenario C :48 PM Base :00 PM Scenario C :15 PM When analyzed individually, widespread residential rain garden implementation (Scenario A) has a more significant impact on both peak and volume of discharge than green roof implementation in commercial and public sectors. Despite the smaller size of rain gardens, this result is reasonable given the greater total area associated with residential land (35%) than commercial and public land (10%). The difference in the spatial extent of rain garden and green roof feature implementations is a valid assumption regardless of land use ratios because rain gardens are much smaller features and can be retrofit into a larger variety of existing spaces. Extensive green roofs are typically only feasible retrofits for shallow-sloping roofs associated with large buildings. The very low

75 % Reduction in Peak % Reduction in Volume 75 volume reductions from the green roof scenario (Scenario B) can also be attributed to the shallow nature of the extensive green roof soil media. The 24-hour duration storms used in this analysis represent a high estimate of rainfall duration. Considering instead the smallest duration storms predicted by an SCS distribution, six-hour storms, of equal frequency and lower magnitude (see Table for rainfall totals), LID Scenario C affords only a slightly greater degree of infiltration storage than the base scenario and almost identical peak discharge responses (Figure 4.1.3). In both cases, peak rainfall intensity occurs close to the time when saturation infiltration rate has been reached, thus producing similar degrees of runoff during the high-intensity temporal center of the storm. Duration Comparison of Peak Reduction for LID Scenario C Duration Comparison of Volume Reduction for LID Scenario C 18% 18% 16% 16% 14% 14% 12% 12% 10% 10% 8% 8% 6% 6% 4% 4% 2% 2% 0% 10 YR 5 YR 2 YR 0% 10 YR 5 YR 2 YR Storm Frequency Storm Frequency 6 HR Design Storm 24 HR Design Storm 6 HR Design Storm 24 HR Design Storm Figure Scenario LID C Reductions in Peak and Discharge for 6- and 24-Hour Storms of Equivalent Frequency.

76 76 It is important to reiterate three assumptions that may be causing an overprediction of volume losses: a full (100%) implementation simulation, no initial saturation, and the disconnection of impervious surface modeling method for rain gardens, of which limitations were discussed in Chapter One of the driving assumptions behind this scenario-based analysis of retrofitscale LID features is their association with a specific land use practice. The assumption of 100% implementation of LID retrofits within each land use area (residential, commercial, and public) is unlikely due to a number of currently-unknown factors including the degree of political and social influence required to reach that level of voluntary implementation, the physical layout required to implement such features, and the economic cost associated with construction and maintenance. However, assuming 100% implementation within these land-use sectors is useful in that it represents the maximum peak and volume reductions possible at the watershed outlet as a first-order investigation of the cumulative hydrologic effectiveness of LID features. At this point, any variation in the degree of implementation within each land use category would be arbitrary, and, as will be discussed later on, the variability in location of the distributed features within the watershed would have an impact on hydrologic response. Another important assumption is the degree of initial saturation. Although unlikely in nature, a uniform initial saturation value of zero was chosen for two reasons: The first reason involves the use of design storms rather than actual storms for this eventbased analysis. Design storms have no associated initial saturation since they are

77 77 theoretical by design. Any assumption of initial saturation would again be arbitrary unless a continuous simulation was run looking at storm events in succession. The second reason for using no initial saturation is that the purpose of this analysis was to determine the maximum possible benefit of distributed LID features in a watershed. By assuming unsaturated soils, soils are allowed to infiltrate to their full capacity. Therefore this event-based analysis operated under the assumption of antecedent dry conditions. This is not entirely unfounded given that the average duration between rainfall events in 2013 was approximately six days 11. However, the sensitivity of the modeled results to variations in initial saturation was tested anyway and will be discussed in the following section LID Feature Parameter Analysis The methodology for this analysis required a number of simplifying assumptions to be made. This is not an unusual phenomenon in large-system modeling efforts, but does require some additional testing to understand the implications that these assumptions have on the model outcome. The measured hydrologic performance of LID scenarios in this analysis is largely determined by the LID feature design parameter assumptions. The question then becomes to what degree would alterations of those parameters affect the hydrologic response of the watershed? 11 Based on the daily rainfall record for HCFCD gauge 540 in White Oak Bayou from to

78 78 As mentioned previously, averaged parameters for each LID cell are proportional to initial values (and imperviousness in the case of residential LID cell). Thus the relative degree of change is not uniform across the watershed and instead exists within a range depending upon the initial value (see Appendix 8 for ranges of individual parameters). By incrementally increasing these amended LID Scenario C parameters uniformly by multiplication factors, we can understand the sensitivity of the model to small or large changes in the LID feature assumptions. Figure shows the possible increases in peak discharge and volume reduction relative to the Scenario C results (i.e. parameters used in Scenario C multiplied by factors from zero to five) under 10-year/24-hour rainfall conditions. Only reasonable 12 multiplication factors were used. These multiplication factors are representative of both a change in the parameters associated with individual LID features (i.e. higher conductivity soil media) and/or a change in the size of the individual features within a cell, since total cell parameters are dependent upon both size and LID design assumptions such as soil media characteristics and roughness. The sensitivity of peak timing was not included because no significant trends were found. This is likely due to the fact that no changes were made to the roughness or configuration of the channels themselves, and that the spread of overland LID cells is relatively even across the drainage network. 12 For example, surface roughness was only increased up to a factor of 1.4 to prevent any roughness increases beyond the reasonable limit of 0.8, while hydraulic conductivity was increased up to five times since it has a much larger theoretical upper limit.

79 Peak Flow Reduction Volume Reduction 79 16% 14% 12% LID Feature Parameters - Peak Discharge LID Feature Parameters - Volume 16% 14% 12% 10% 10% 8% 8% 6% 6% 4% 4% 2% 2% 0% Multiplication Factor 0% Multiplication Factor Figure Sensitivity Analysis of LID Feature Design Parameters by Land Use. For both residential (R) and commercial/public (CP) LID features, peak discharge is more sensitive than volume to changes in design parameters. Changes in peak are largely driven by hydraulic conductivity and imperviousness. However, this sensitivity analysis shows that increasing hydraulic conductivity and roughness or decreasing imperviousness by a factor of two would cause only a maximum additional 8% peak flow reduction for the 10-year storm event. Thus overall the sensitivity of the model to changes in these parameters is not significant.

80 80 The low sensitivity of volume is evidenced by a five-fold increase in hydraulic conductivity only causing a maximum possible increase in volume of about 5%. This implies that the modeled Scenario C has already roughly maximized infiltration relative to the given rainfall. Abstraction (see Chapter for a discussion of abstraction) was altered for this sensitivity analysis to reflect increased surface storage capacity of rain gardens, but was not included in the results because no change was measured, even when abstraction was increased to 0.05 inches abstraction per cell (equivalent to 24 inches per rain garden). This is likely due to the nature of the abstraction feature in Vflo model. It is only removing a set depth of rainfall from the runoff equation and not accounting for retention of upstream inflow. Any additional volume reduction would therefore need to be addressed using additional storage features, such as sub-surface detention, modeled with a specified storage-discharge relationship. Overall, for the LID cells corresponding to residential rain gardens evaluated and altered for Scenario A, changes in infiltration rate (represented as hydraulic conductivity) most significantly affect both the peak and volume of discharge measured at the watershed outlet relative to changes in other parameters. Initial saturation was also altered in increments of 0.25 up to full saturation (1.00). Initial saturation was changed equivalently for the LID and Base models so that hydrologic performance was measured under the same initial saturation conditions for both. It was found that initial saturation had a very minimal effect on the hydrologic performance of LID features until a relatively high level of about 0.5 to 0.7 was reached.

81 % Reduction from Base Model 81 Beyond this point, the ability for LID features to cause any cumulative reduction in volume or peak decreased rapidly to a near-zero reduction capacity at full saturation. Sensitivity Analysis - Initial Saturation 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% Initial Saturation Volume Reduction Peak Reduction Figure Model Sensitivity Analysis of Initial Saturation under LID Scenario C and 10YR/24HR Rainfall Conditions. This outcome is not surprising given that infiltration storage is the main mechanism for reduction of volume, which directly impacts peak discharge. It is therefore true that the degree of soil moisture will affect the hydrologic performance of LID features at the watershed scale, but only under high initial saturation conditions. Except in the case of back-to-back rainfall events, however, we can assume that typical initial saturation values would be below this threshold. Therefore for event-based simulations with dry antecedent conditions, the assumption of zero initial saturation does

82 82 not significantly impact the results. For future continuous simulations, however, soil moisture must be assigned more carefully The Effects of Spatial Rainfall Distribution Thus far, spatially-uniform design rainfalls have been used to simulate the hydrologic performance of LID features in White Oak Bayou. However, naturally occurring storm events are both spatially and temporally distributed and typically move as fronts across a watershed. Variations in the direction, radius, velocity, and total rainfall of a single storm cell influence the characteristics of overland flow in a watershed (de Lima & Singh, 2002). The full impact of these rainfall variations on LID performance cannot be captured using uniform design rainfalls or even a few spatially-distributed historical storm events. Although a more thorough analysis of the impact of storm characteristics on distributed LID feature performance was outside the scope of this investigation, it is theorized that the fairly evenly distributed nature of rain gardens and green roofs in White Oak Bayou Watershed may cause their hydrologic performance to be less sensitive to these variations. The larger spatial coverage allowed by distributed LID features, as compared to clustered or regionally-sized features, causes rainfall interception over a larger spatial extent of the watershed. Therefore in theory, runoff from areas of higher rainfall intensity will be more likely to be captured by LID features regardless of location or direction of storm cell movement. As a preliminary investigation into this idea, a comparison of a single spatiallyuniform design rainfall and a historic gauged event of roughly equal duration and

83 Rainfall Intensity (in/hr) 83 magnitude was performed. The 24-hour duration storm event which occurred on September 20 th to 21 st, 2013 (see Chapter for details), had an average total rainfall of four inches, which is approximately equivalent to a 2-year 24-hour design storm in this area (4.4 inches). Average temporal rainfall distributions of both the observed (averaged across rain gauges for display purposes) and the synthetic storm (SCS Type III) are very similar and only differ in peak timing by about two hours (Figure 4.3.1) in/ 24 hr Design and Observed Rainfalls YR/24 HR (4.4 in/24 hr) SEPT 2013 (Vflo) Time (hr) Figure Comparison of Historic (Sept. 2013) and Theoretical (SCS Type III) Temporal Rainfall Distributions for a 4 in/24 hr Storm in White Oak Bayou. Because the two storms show very similar hyetographs, it would be reasonable to expect that the watershed response would be similar in both storms if the rainfalls were equivalently distributed across the watershed. The non-spatially-uniform distribution of

84 Minutes 84 the September 2013 storm, however, elicits about 5% more peak flow reduction than the design storm of equal magnitude, and an equal volume reduction (Figure 4.3.2). 2YR/24HR Design Storm and Sept 2013 Storm LID Scenario C Hydrologic Performance - Peak Flow and Timing 2YR/24HR Design Storm and Sept 2013 Storm LID Scenario C Hydrologic Performance - Timing 25% 30 20% 25 15% 20 10% 5% 0% Change in Peak Flow Change in Volume Change in Peak Timing Design Storm SEPT 2013 Storm Design Storm SEPT 2013 Storm Figure Comparison of LID Scenario C Reduction in Peak, Volume, and Timing of Runoff Between Spatially-Uniform and Spatially-Distributed Storm Events. As opposed to previous volume-based reductions in peak flow observed during uniform desigbn storms, the greater peak flow reduction for the September 2013 storm is caused by a greater delay in timing of runoff. The spatially-distributed nature of the storm created areas of higher intensity rainfall near the upper-end of the watershed and the Brickhouse Gully and Cole Creek drainage areas (Figure 4.3.3). Spatial variation in runoff production was therefore not solely dependent upon soil infiltration rates, but also dependent upon local rainfall intensities. Areas of lower intensity rainfall had a greater delay in the production of runoff due to the increased infiltration capacity and roughness

85 85 of LID features which caused an overall delay and reduction in peak flow relative to the uniform rainfall condition. Cole Creek Brickhouse Gully Figure Spatial Distribution of Total Rainfall During September 2013 Storm Event in White Oak Bayou Watershed. Although this finding only represents a preliminary investigation into the effects of rainfall patterns on the ability for distributed LID features to reduce hydrologic impacts, it indicates that these features may have the ability to perform better or equivalently under conditions of variable spatial rainfall patterns than under theoretical uniform rainfall conditions

86 86 Another important finding from this comparison is that a watershed response to LID Scenarios is lower in terms of peak and volume of runoff during a spatiallydistributed historic rainfall, thus the use of design rainfalls may represent a conservative approach to evaluating hydrologic performance Distribution Analysis of Subcatchment Response Thus far, the results of the distributed model simulations of small-scale Low Impact Development (LID) features in the White Oak Bayou watershed have focused on cumulative reductions measured at the outlet. Because all subcatchments within White Oak Bayou eventually drain to the same point (outlet), the measurement of hydrologic response at the outlet gauge accounts for the entire LID feature network. What cannot be measured at the outlet, however, are local subcatchment-level responses. These can be variable from the outlet response depending upon the number and layout of upstream LID features. Previous studies using the Vflo model have indicated that the location of flood control features, such as detention ponds, in relation to the watershed outlet have a significant impact on watershed response (Fang, et al., 2010). It is not understood, however, how the location of small distributed features, such as urban-scale LID, affect watershed response. Though individually accounting for less storage or reduction in peak discharge than detention ponds, the small-scale nature of these features allow them to be located more widespread across a drainage network and have a larger range of possible layouts.

87 87 To better understand the impacts of LID feature layouts, the hydrologic responses at the outlets of three tributary subcatchments within White Oak Bayou were analyzed. These gauges are located near the outlets of Cole Creek ( ), Brickhouse Gully ( ), and Little White Oak Bayou ( ) (Figure 4.4.1). Figure Map of Cole Creek, Brickhouse Gully, and Little White Oak Bayou Subwatersheds and their Respective Stream Gauges. Considering the combination of rain gardens and green roofs associated with Scenario C, the total density and location of LID features varies with respect to the outlet

88 88 within each of these three subcatchments. Figure shows the distribution of LID cells (by land use) within each catchment compared to the distribution in White Oak Bayou Watershed as a whole. The generally even distribution of LID cells within Little White Oak Bayou most closely resembles that of the larger watershed. Features within Brickhouse Gully are relatively evenly distributed close to the main channel, while features within Cole Creek are mostly centralized near the outlet. Thus these subcatchments approximately represent an even distribution (Little White Oak), a channel-centered distribution (Brickhouse Gully), and an outlet-grouping distribution (Cole Creek).

89 89 White Oak Bayou (Entire Watershed) Proportion of LID Area Residential 34% Non LID 60% Commercial 2% Public 4% Figure Proportion of Land Use Areas Associated with LID Features under the RCP1 LID Development Scenario in the White Oak Bayou Watershed, Cole Creek, Brickhouse Gully, and Little White Oak Bayou Subwatersheds.

90 % Reduction in Peak Discharge % Reduction in Volume 90 Evaluating LID Scenario C during the spatially-uniform 10- to 2-year/24-hour design storms, the response at each subcatchment varies by about 10% (Figure 4.4.3). The grouping of LID cells near the outlet in Cole Creek had the lowest capacity to reduce hydrologic impacts, while an even distribution of LID cells in Little White Oak Bayou had the highest. Reduction of Peak Discharge in Tributary Subcatchments compared to Base Model 30% 25% 20% 15% 10% 5% Reduction of Volume in Tributary Subcatchments compared to Base Model 30% 25% 20% 15% 10% 5% 0% Little White Oak Bayou Brickhouse Gully Cole Creek 0% Little White Oak Bayou Brickhouse Gully Cole Creek 10 YR 5 YR 2 YR 10 YR 5 YR 2 YR Figure Hydrologic Performance of LID Scencario C for 10 to 2 Year Design Storms Compared at the Tributary Outlets. To fully identify the impact of the layout of LID features in a watershed, a more controlled experiment would need to be set up using identical subwatersheds and quantities of features placed in varying locations throughout the watershed. However, within the limitations of this study, it appears that a more even distribution of LID

91 91 features across the flow networks may have a greater ability to control peak discharge and volume of runoff than centralized features near the watershed outlet. The results for Brickhouse Gully also indicate that locating LID features close to the main channel, thus simulating an urban riparian corridor -type distribution, will have a similar impact to evenly distributing them across the watershed. This theory requires further experimental validation, but may have significant implications for future planning of LID feature developments.

92 92 Chapter 5 Conclusions A fully-distributed GIS-based hydrologic model of the White Oak Bayou Watershed in Houston, Texas was successfully set-up using distributed data, calibrated, and utilized to evaluate urban-scale retrofit LID features. The purpose of the study was to represent and evaluate the cumulative hydrologic impacts of theoretical changes in a large watershed associated with two infiltration-based LID features: rain gardens and green roofs. A novel interpretation of a weighted averaging approach was proposed to address the very high resolution distributed nature of urban LID retrofit features within individual 2.8-acre grid cells of the Vflo model. A range of frequencies of spatiallyuniform design storms based on the SCS Type III temporal distribution were used to evaluate the ability of LID features to cumulatively reduce peak discharge, volume of runoff, and timing at the watershed outlet. In summary, this study on the modeled hydrologic performance of distributed LID features in a high intensity rainfall region resulted in the major conclusion that infiltration-based distributed LID features have little

93 93 capacity to control hydrologic impacts during low frequency storms (i.e. 100-year storm), but have increasing benefits as the magnitude and frequency decreases and can cumulatively reduce peak and volume of discharge by 13% and 8% respectively for the 10-year flood event Major Findings Because this study represents only a first-order analysis of watershed-scale LID features in a high intensity rainfall urban environment, several lingering questions will need to be addressed through future research endeavors. However, this study has successfully addressed the primary goal of utilizing a fully-distributed hydrologic model to evaluate watershed-wide retrofit-scale LID feature performance in a high intensity rainfall region, thereby producing the following major findings: 1. Cumulatively in a large watershed, infiltration-based LID features have the ability to reduce peak discharge by 13 to 18% and volume by up to 10% for moderate frequency, high intensity rainfall conditions characteristic of the Houston region (see Figure 4.1.2). 2. Volume reduction from LID-enhanced infiltration is the driving cause of peak flow reduction during single rainfall events, more so than timing delays from detention or increased roughness. 3. Rain gardens constructed in residential areas have a greater cumulative hydrologic benefit than green roofs constructed in commercial and public areas due to their

94 94 greater infiltration capacity and ability to drain upstream impervious areas. Though individually covering a larger area, the shallow soils and disconnection from upstream drainage areas associated with extensive green roofs give them a lower event-based capacity to reduce hydrologic impacts. 4. LID features would require significantly more storage capacity to be used as flood control features for extreme events (100-year), even when implemented at the watershed-scale (see Figure 4.1.2). Despite the low level of impact for the 100-year event from the maximum LID implementation scenario, Scenario C (combination of rain gardens and green roofs), the reductions in peak flow and volume for the smaller 10-year event likely translate to a significant reduction in floodplain extent (Figure 2.2.3). When looking at the distribution of impacts along individual channel segments in relation to the floodplain, the largest reductions in peak and volume appear to coincide with the most significantly inundated subcatchments (Upper White Oak Bayou, Brickhouse Gully, and Little White Oak Bayou). Table LID Scenario C Hydrologic Impact Reductions for the 10-year/24- hour Storm Event at Each Subcatchment in White Oak Bayou Watershed. % Reduction in Peak Discharge % Reduction in Volume of Upper White Oak Bayou ( ) Lower White Oak Bayou ( ) Outlet ( ) Cole Creek ( ) Brickhouse Gully ( ) Little White Oak Bayou ( ) 13% 11% 13% 8% 17% 16% 10% 10% 8% 4% 12% 11%

95 95 Runoff A preliminary steady-state analysis of these reduced flows using the published HCFCD HEC-RAS model for main channel of White Oak Bayou (E ) indicates that LID Scenario C could cause an average 1.1-foot decrease in water surface elevations in the main channel during a 10-year flood event. For planning purposes this analysis would require further validation, but indicates that distributed urban-scale LID features may be an effective flood control tool to mitigate localized flooding from higherfrequency (lower magnitude) flood events Future Work Another important aspect of this analysis is the application of the Vflo distributed model as a tool to evaluate LID features at the urban watershed scale. Although the modeling framework has previously been utilized to represent conservationbased LID approaches such as wetlands and riparian buffers (Duncan, 2011; Doubleday, et al., 2013), and flood control features such as regional detention ponds (Fang, et al., 2010), the small sub-cell size of urban LID retrofits such as rain gardens and green roofs required a number of modifications to be made. Chief among these is the assumption of no additional subsurface storage capacity in the rain garden features. For flood control purposes, it is likely that LID features would be constructed with additional detention storage capacity, which would require the input of a specific storage-discharge relationship in the Vflo model. However, because these features are

96 96 smaller than an individual grid cell, an inclusion of storage-discharge relationships would require either a higher resolution model (i.e. splitting the Vflo model into individual models of higher-resolution subcatchments) or the development of a relationship between the storage-discharge curve of an individual feature and the resulting storage-discharge of its associated cell via a series of LID modules. The former method would require a cumbersome linking of several small-scale models, while the latter method is problematic in that it requires an informed assumption as to the layout of features within the cell that will strongly influence the resulting hydrologic performance. However, this is an important issue that needs to be addressed to improve the accuracy and simplicity of small-scale representations of LID in distributed models. The lack of detention storage in the current modeled LID scenarios underestimates the potential peak flow timing delay which would have an additional flood control benefit. The distributed nature of the Vflo model and its ability to be easily coupled with high resolution GIS datasets make it an ideal software for upscaling LID feature implementation to the watershed-level. It is much better for large watershed analyses than models such as SWMM, which are specifically designed to be used for small-scale infrastructure analyses, such as pipes and gutters, and which would have a significant degree of uncertainty at the large watershed scale due to the level of detail required. However, unlike SWMM, Vflo is not currently set up to directly account for the infiltration of upstream runoff via impervious-to-pervious surface routing. This study attempted to correct for this limitation by expanding the area associated with LID infiltration to include upstream impervious drainage. As was noted earlier, however, this

97 97 may overestimate the total rainfall infiltrated and does not account for flow attenuation associated with increased localized roughness within a cell. A sensitivity analysis of parameters in the model showed that the watershed is most sensitive to changes in imperviousness and hydraulic conductivity associated with rain gardens, which drive infiltration and volume reduction, and thus peak flow reduction. The impact of initial soil saturation when it is above 60% will also become a more important factor when running continuous simulations on back-to-back storm events. For continuous simulations, however, feature maintenance should also be considered to account for degradation of infiltration capacity over time due to clogging. This analysis assumed features were operating at full design capacity at the start of each rainfall event. Follow-up investigations on LID feature modeling assumptions, the nature of rainfall distributions, and the location of LID features in this study identified possible additional benefits of distributed urban LID networks when evaluated for cumulative hydrologic impacts. A comparison of long (24-hour) and short (6-hour) duration storm events of equivalent probability resulted in similar hydrologic reductions from LID features due to the temporal distribution and associated high rainfall intensities in relation to soil infiltration rates. A variation in spatial distribution using one historic gauged event from September 2013 showed similar results. Ideally, this analysis would be repeated using various temporal and spatial rainfall distributions more representative of actual observed storm events in the Houston area. Unfortunately, the relatively low-frequency of storm

98 98 events evaluated (10% - 50% probability of occurrence) make it difficult to find actual historic events of similar magnitude and duration. An extension of this project would involve running a suite of equivalent magnitude theoretical spatial distributions of rainfall over the watershed along with several equivalent frequency storms of varying temporal distribution (single peaked, double peaked, SCS, front-loaded, etc.) to measure LID performance over the full range of possible rainfall conditions. Another preliminary finding that merits further investigation is the theory that the location of distributed LID features in the watershed impacts the resulting hydrologic performance. Results from land use based distributions of LID features in the White Oak Bayou watershed indicate that features distributed along the longitudinal axis of a drainage network are more effective at reducing hydrologic impacts than features grouped near the outlet. The small-scale nature of the rain gardens and green roofs modeled, as compared to large-scale flood control features such as regional detention ponds, means that some theoretical groupings may be unrealistic in the Houston urban environment due to a lack of zoning regulations and their assumed association with specific land uses. An ongoing extension of this study should be able to partially validate these initial findings by comparing the cumulative response of the distributed theoretical layout of retrofit LID features in the unzoned White Oak Bayou watershed to a more centralized theoretical layout in a nearby watershed which does have land use zoning restrictions. Finally, a discussion of water quality impacts is absent from this investigation. An important benefit that LID features offer over conventional stormwater controls is

99 99 enhanced water quality of runoff through natural adsorption, sedimentation, biologic, and filtration processes. In fact, many site-scale monitoring studies couple hydrologic and water quality performance (Bedan & Clausen, 2009; USEPA, 2000). The flood-prone nature of the Houston area and the complexity of the distributed model used to evaluate hydrologic peformance directed the focus of this study away from water quality impacts and instead towards quantifying cumulative hydrologic impacts. Although minimal data currently exists on the water quality performance of LID features in the Houston area for reduction of the local contaminants of interest, pathogens, current monitoring studies are underway at recently constructed sites, including the Cottage Grove rain garden site in the White Oak Bayou Watershed, to help evaluate the degree to which LID features can improve water quality in the bayous. The development of pollutant loading curves for LID features in this area will help inform an analysis of the potential reduction in pathogen contamination in the bayous from overland runoff intercepted and filtered through LID features. Future research should attempt to couple the hydrologic benefits modeled in this study with potential water quality reductions to evaluate whether bayou water quality enhancement could be used as an additional incentive for LID implementation beyond flood control, but should focus on lower magnitude, higher frequency storm events.

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102 102 Woodlands, Texas. Journal of the American Water Resources Association, December, 49(6), pp Duncan, B. R., The impact of palustrine wetland loss on flood peaks: an application of distributed hydrologic modeling in Harris County, Houston: Rice University. Elliott, A. & Trowsdale, S., A review of models for low impact urban stormwater drainage. Environmental Modeling & Software, Volume 22, pp Emerson, C., Welty, C. & Traver, R., Watershed-scale evaluation of a system of storm water detention basins. Journal of Hydrologic Engineering, Volume 10, pp Fang, Z. et al., Using a distributed hydrologic model to evaluate the location of urban development and flood control storage. Journal of Water Resources Planning and Management, 136(5), pp Getter, K. L., Rowe, D. B. & Andresen, J. A., Quanitfying the effect of slope on extensive green roof stormwater retention. Ecological Engineering, Volume 31, pp Gilroy, K. & McCuen, R., Spatio-temporal effects of low impact development practices. Journal of Hydrology, Volume 367, pp Hathaway, A. M., Hunt, W. F. & Jennings, G. D., A Field Study of Green Roof Hydrologic and Water Quality Performane. Transactions of the American Society of Agricultural and Biological Engineers, 51(1), pp HCFCD, Policy Criteria & Procedure Manual, Houston: Harris County Flood Control District. HCFCD, Charting Buffalo: A Collaborative Study of Buffalo and Lower White Oak Bayous, Houston: Harris County Flood Control District. HCFCD, Harris County Flood Control District: White Oak Bayou Watershed. [Online] Available at: [Accessed 23 August 2014]. HCFCD, H. C. F. C. D., Model and Map Management (M3) System. [Online] Available at: [Accessed 14 August 2014]. HCFCD, n.d. Hurricane Ike: The Storm. The Facts. [Online] Available at: [Accessed 31 October 2014]. Hershfield, D. M., Technical Paper No. 40: Rainfall Frequency Atlas of the United States for Durations from 30 Minutes to 24 Hours and Return Periods from 1 to 100 Years, Washington D.C.: U.S. Department of Commerce. HGAC, H. G. A. C., Regional Land Use Information System, Houston: HGAC.

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104 104 Mentens, J., Raes, D. & Hermy, M., Green roofs as a tool for solving the rainwater runoff problem in the urbanized 21st century?. Landscape and Urban Planning, Volume 77, pp NRC, Urban Stormwater Management in the United States, Washington, D.C.: National Academies Press. NRCS, Urban Hydrology for Small Watersheds, s.l.: United States Department of Agriculture. NRCS, Engineering Properies: Harris County, Texas, s.l.: USDA National Resources Conservation Service. Phillips, D. & Marks, D., Spatial uncertainty analysis: propagation of interpolation errors in spatially distributed models. Ecological Modelling, 91(1-3), pp Rawls, W., Brakensiek, D. & Miller, N., Green-Ampt infiltration parameters from soils data. Journal of Hydraulic Engineering, 109(1), pp Rogers, G. & DeFee II, B., Long-term impact of development on a watershed: early indicators of future problems. Landscape and Urban Planning, Volume 73, pp Selbig, W. & Bannerman, R., A comparison of runoff quantity and quality from two small basins undergoing implementation of conventional- and low-impact-development (LID) strategies: Cross Plains, Wisconsin, water years , Reston, Virginia: U.S. Geological Survey. Semadeni-Davies, A., Winter performance of an urban stormwater pond in southern Sweden. Hydrological Process, Volume 20, pp Stovin, V., Vesuviano, G. & Kasmin, H., The hydrological performance of a green roof test bed under UK climatic conditions. Journal of Hydrology, Volume 414, pp Teague, A., Christian, J., Bedient & P., Radar rainfall application in distributed hyrologic modeling for Cypress Creek Watershed, Texas. Journal of Hydrologic Engineering, 18(2), pp TSARP, Recommendation for: Rainfall Amounts in Harris County, s.l.: Tropical Storm Allison Recovery Project. TWDB, T. W. D. B., Water for Texas: Rainwater Harvesting. [Online] Available at: [Accessed 25 July 2014]. USACE, HEC-GeoRAS, an extension for support of HEC-RAS using Arcview, s.l.: U.S. Army Corps of Engineers Hydrologic Engineering Center. USEPA, Results of the nationwide urban runoff program, s.l.: United States Environmental Protection Agency.

105 105 USEPA, Low Impact Development (LID): A Literature Review, Washington, DC: United States Environmental Protection Agency. USEPA, Best Management Practices (BMPs). [Online] Available at: [Accessed 4 August 2014]. USEPA, U. S. E. P. A., Stormwater Technology Fact Sheet: Bioretention. [Online] Available at: [Accessed 3 August 2014]. USEPA, U. S. E. P. A., What is a Rain Barrel?. [Online] Available at: [Accessed 5 August 2014]. USEPA, U. S. E. P. A., Green Roofs. [Online] Available at: [Accessed 4 August 2014]. VanWoert, N. D. et al., Green roof stormwater retention: Effects of roof surface, slope, and media depth. Journal of Environmental Quality, Volume 34, pp Vieux, Inc., Vflo(TM). [Online] Available at: [Accessed 16 November 2014]. Vieux, B. & Bedient, P., Estimation of rainfall for flood prediction from WSR-88D refelctivity: A case study, October Weather and Forecasting, 13(2), pp Vieux, B., Bralts, V., Segerlind, L. & Wallace, R., Finite Element Watershed Modeling: One-Dimensional Elements. Journal of Water Resources Management and Planning, November/December, 116(6), pp Vieux, B. E., Distributed Hydrologic Modeling Using GIS. 2nd ed. Norwell, MA: Kluwer Academic Publishers. Vieux, B. & Gauer, N., Finite-Element Modeling of Storm WAter Runoff Using GRASS GIS. Computer-Aided Civil and Infrastructure Engineering, 9(4), pp Volder, A. & Dvorak, B., Event size, substrate water content and vegetation affect strom water retention efficiency of an un-irrigated extensive green roof system in Central Texas. Sustainable Cities and Society, Volume 10, pp Voorhees, M. & Wenzel, H., Urban Design-Storm Sensitivity and Reliability. Journal of Hydrology, Volume 68, pp

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107 Increase in Timing (hrs) % Increase in Volume % Increase in Peak 107 Appendix 1. Green and Ampt Infiltration Parameters Sensitivity Analysis Results for White Oak Bayou Base Model. Initial soil saturation values ranged from 0 to 1. Hydraulic conductivity and Initial saturation had the most significant, but opposite impacts on volume, peak, and timing. Sensitivity Analysis - Infiltration Volume 20% 15% Sensitivity Analysis - Infiltration Peak 20% 15% 10% 5% 0% -1-5% % 10% 5% 0% -1-5% % -15% Calibration Factor -15% Calibration Factor Sensitivity Analysis - Infiltration Timing Calibration Factor Soil Depth Wetting Front Hydraulic conductivity Initial Saturation

108 Final Calibration Factors for Each Catchment Area in White Oak Bayou. Values were calculated as the product of each calibration factor adjustment for Hurricane Ike, the July 2012 storm, and the September 2013 storm. Final Calibration Factors Catchment Overland Channel Hydraulic Wetting Soil Imperviousness Area Roughness Roughness Conductivity Front Depth Upper White Oak Lower White Oak + Outlet Cole Creek Brickhouse Gully Little White Oak Land Use Distribution in White Oak Bayou Watershed (HGAC, 2011)

109 SCS Rainfall Distribution Curves by Type (NRCS, 1986). Rainfall patterns in Houston follow a Type III distribution.

110 Rainfall Intensity (in/hr) Hyetographs of the 6- and 24-hour design rainfalls in Harris County, TX. Temporal distributions are calculated from the SCS Type III Rainfall Distribution (TSARP, 2009). 12 SCS Type III Hyetographs for Harris County, TX (6-min increments) Time (hr) 100 yr/24 hr 10 yr/24 hr 5 yr/24 hr 2yr/24 hr 10 yr/6 hr 5 yr/ 6 hr 2 yr/ 6 hr 6. Calibration hydrograph results for all gauges by storm event (gauges and are shown in Chapter 2.3.3: 1. Hurricane Ike:

111 111

112 2. July 2013: 112

113 3. September 2013: 113