Monitoring and Modeling of the Hydrologic Performance of the Carroll Street. Right-of-Way Bioswale. Wei Chen

Transcription

1 Monitoring and Modeling of the Hydrologic Performance of the Carroll Street Right-of-Way Bioswale Wei Chen Master of Science in Environmental Engineering August 2014 Department of Civil, Architectural, and Environmental Engineering Drexel University

2 ACKNOWLEDGMENTS Franco Montalto, PhD, PE (Academic Advisor) Kimberly DiGiovanni, PhD, EIT (Mentor) Scott Jeffers, PhD Candidate Xiwang Li, PhD Candidate

3 i TABLE OF CONTENTS LIST OF FIGURES... ii LIST OF TABLES... v ABSTRACT... vi INTRODUCTION... 1 i. Background... 1 ii. Literature Review... 5 METHODS... 8 i. Site Description... 8 ii. The Standard Design of ROW Bioswale... 9 iii. Monitoring Equipment iv. Calculations of Observed Infiltration Rates v. Modeling Platforms: SWWM Model vi. Visual MODFLOW Model RESULTS i. On Site Monitoring Result ii. Result of Darcy s Law Analysis iii. SWMM Model Result iv. Visual MODFLOW RESULTS DISCUSSION CONCLUSIONS LIST OF REFERENCES... 54

4 ii LIST OF FIGURES Figure 1: Combine Sewer Overflow in Urban Area (NYCDEP, 2013)... 2 Figure 2: A schematic overview of the SWMM and Visual MODFLOW model setup process... 7 Figure 3: Location of ROW bioswale and the Carroll Street Catchment Area (Google 2014)... 8 Figure 4.Standard design drawings for bioswale type one (NYCDEP, 2013) Figure 5: Cross section view of bioswale type one (NYCDEP, 2013) Figure 6: Schematic Cross Section View of Piezometers in Bioswale Figure 7: Schematic Layout of Piezometers in Bioswale Figure 8: Schematic diagram of Darcy s Law Figure 9: The system layout consists of four sub-catchment areas S1 through S4. The system discharges to catch basin the point labeled Outfall Figure 10:Time series of rainfall intensities for 1inch 1 hour, 8 hours and 24 hours Figure 11: Time series of rainfall intensities for Figure 12: Model configuration of Carroll Street. The bioswale is representation by 1.52m x 6.1 m cells Figure 13:Cross-section View of Visual MODFLOW Model Figure 14: Pressure transducers reading (raw data) for piezometric head in 4/30/ Figure 15: Observed the Piezometer Head of 9/21/2013, 9/12/2013, 11/26/2013, 12/29/2013, 3/12/2014, 3/29/2014 and 4/30/

5 iii Figure 16: Infiltration Rate Was Calculated with Darcy s Law Figure 17: The average of optimized values of parameters from four rain events. Calibration focused on adjustment to hydraulic conductivity and porosity value, field capacity, and vegetation volume fraction Figure 18: Comparison of Observed Infiltration Rates and SWMM Infiltration Rate Figure 19: Comparison of cumulative infiltration from SWMM 5 and Observed.. 35 Figure 20: Stormwater Volume Retained by the ROW bioswale for one inch, 1 hour, 8 hours and 24 hours rain events Figure 21: A comparison between pre-development (without bioswale) and postdevelopment (with bioswale) conditions for the 1inch, 8 hour storm Figure 22: Comparison of pro-development and post-development condition for cumulative runoff in Figure 23: Stormwater Volume Retained by the ROW bioswale for 57 rain events in Figure 24: Stormwater Volume Retained by the ROW bioswale for one inch, 1 hour, 8 hours and 24 hours rain events Figure 25: Stormwater Volume Retained by the ROW bioswale for one inch, 1 hour, 8 hours and 24 hours rain events (Plot box and summery table) Figure 26: Box and whisker plot comparing R% (Percent of Tributary Area Runoff Retained per event) from bioswales under of a variety of HLR for 1988 precipitation data Figure 27: Cumulative infiltration versus time for different HLR values...42

6 iv Figure 28: Demonstrate surface water elevation contour (top) and height of groundwater mound (bottom) elevation for 1 inch, 1 hours rain events Figure 29: Demonstrate surface water elevation contour (top) and height of groundwater mound (bottom) elevation for one inch, 8 hours rain events Figure 30: Demonstrate surface water elevation contour (top) and height of groundwater mound (bottom) elevation for one inch, 24 hours rain events Figure 31: Box and whisker plot comparing the height of ground water mounds of a variety of location across the street for 1988 precipitation. Note: the water table is assumed 6.1 m below the surface

7 v LIST OF TABLES Table 1: Event rainfall and duration Table 2: A comparison between pre-development and post-development for 1 inch, 8 hours and 24 hours storm Table 3: Comparison of pro-development and post-development condition for cumulative runoff in Table 4: Summary for one inch, 1 hour, 8 hours and 24 hours rain events... 40

8 vi ABSTRACT Monitoring and Modeling of the Hydrologic Performance of the Carroll Street Right-of-Way Bioswale Wei Chen Franco Montalto, PhD Like many old cities in the United States, New York City (NYC) suffers from combined sewer overflows (CSOs) where untreated stormwater and wastewater overflow from sewers during wet-weather rain-events. To manage this environmental issue, New York City is underway with efforts to implement Green Infrastructure (GI) throughout the city which seeks to capture stormwater in green space while reducing the load on sewer systems. The main focus of this project is the application of computer simulated hydraulic models SWWM5 and Visual MODFLOW to evaluate the performance of an actual NYC Department of Environmental Protection Right of Way Bioswale (DEP ROWB). The bioswale performance metrics evaluated are volume reduction (SWMM) and influence on groundwater i.e. groundwater mounding (Visual MODFLOW). The actual ROW bioswale evaluated in this study is installed on Denton place & Carroll Street in Brooklyn, New York. The site is instrumented with monitoring equipment for continuous time-series measurement of piezometric head. Monitoring data used in the analyses occurred over a roughly one year observation period between

9 vii 2013 and Field measured data was used to calculate infiltration rates using Darcy s Law and to calibrate the SWMM model. Results from the calibrated SWMM model were used as inputs to the Visual MODFLOW model to simulate the groundwater mounds beneath ROW bioswale. Models were run on an event basis (using one inch design storms) and continually over one year period (using the 1988 precipitation record). The SWMM Model results reveal that, as currently configured, the ROW bioswale at Carroll Street can capture approximately 6% to 7% of the stormwater runoff generated from its catchment area during a 1 inch (25.4 mm) rain event. This bioswale could capture a higher percentage of the runoff generated by this particular storm, if it had smaller tributary drainage area. The Visual MODFLOW output demonstrates that, during a typical rainfall year, the maximum height of simulated groundwater mounding occurs under the center of the bioswale, and that the average height of all mounds generated during a representative year of rainfall is 0.46 m. In this particular setting, it appears unlikely that this magnitude of mounding could flood basements or negatively impact nearby underground utilities.

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11 1 INTRODUCTION i. Background Like many old cities in the United States, New York City (NYC) suffers from combined sewer overflows (CSOs) where untreated stormwater and wastewater overflows from sewers during wet-weather rain-events. A large percentage of the city is served by combined sewer systems that convey storm water, domestic sewage, and industrial wastewater in the same pipe network. Most of the time, these combined sewer systems transport the flow to wastewater treatment plant for treatment and discharge. However, the combined sewage volume in a combined sewer system can exceed the conveyance capacity of the sewer during periods of intensive or long duration rainfall (Figure 1). The result leads to CSOs during which excess wastewater is discharged directly to nearby water bodies causing pollution (EPA, 2011). The US Environmental Protection Agency (EPA) estimates that approximately 30 billion gallons of CSOs are discharged into New York s water-bodies each year. CSOs are the leading cause of water pollution in NYC s harbor (NYPCD, 2011).

12 2 Figure 1: Combine Sewer Overflow in Urban Area (EPA,U.S, 2004) New York City released the NYC Green Infrastructure (GI) plan in September 2010, which called for the construction of green infrastructure throughout the city to address challenges regarding the management of CSOs (NYCDEP, 2011). The NYC green infrastructure program led by New York City Department of Environmental Protection (DEP), presents an alternative method to improving water quality that integrates green infrastructure such as Right of Way bioswales, stormwater capture green streets, green roofs, rain gardens, permeable pavements and other decentralized stormwater management facilities into the urban landscape. The City s goal is to capture the first inch of rainfall from 10% of the impervious areas in combined sewer watersheds through detention or infiltration techniques over 20 years. The DEP estimates that this

13 3 plan will reduce CSOs by approximately 1.5 bgy (billion gallons per year). The City is prepared to spend up to $1.5 billion over 20 year to implement this green infrastructure plan (Bloomberg, 2011). Green infrastructure has been endorsed by the U.S Environmental Protection Agency (EPA) which calls it an effective response to a variety of environmental challenges that is cost-effective, sustainable, and provides multiple desirable environmental outcomes. (Bloomberg, 2011). As part of NYC Green Infrastructure (GI) plan, DEP offers GI grants to private property owners in combined sewer areas of New York City who wish to install GI. The minimum requirement for eligibility is that the first 1 inch of stormwater runoff from the contributing impervious area is managed. Since 2011, DEP has committed over $11.5 million to 29 Projects (NYCDEP, 2013). This thesis focuses on a GI facility built in Brooklyn as part of this grant program. Two top contributors for storm water runoff to the combined sewer system are streets and sidewalks, which collectively make up approximately 27 percent of the land surface of portions of the city drained by combined sewers (McLaughlin, 2012). Effectively managing right of way runoff is key, since the right of way is almost entirely impervious. One type of GI that is being widely implemented within the right-of-way in New York City is the Right of Way (ROW) bioswale which is designed to manage stormwater from streets and sidewalks (NYCDEP, 2011). Designed and implemented by the NYCDEP, these

14 4 ROW bioswales are installed on sidewalks immediately upstream of the stormwater receiving catch basin. When it rains, storm water flowing down the street and sidewalk are captured and stored by the bioswale preventing water from combined sewer system,which reduce the volume of sewer flow during entering rain events. According to Bioswales Care Hand Book provide by NYCDED, each bioswale can collect an average of 8.5 m³ (2,244 gallons) of water per rain event. To date, more than eight standard design variations of the ROB bioswales have been developed by DEP. Because ROW bioswales are still relatively new, there is a need for more research documenting their stormwater capture performance. There is also a need to evaluate their potential negative impacts on existing underground infrastructure, for example by ground water mounding, which can occur when infiltrated water reach enhances the local water table elevation. This process can occur beneath ROW bioswales, since they are specifically engineered to infiltrate large volumes of stormwater runoff in a small space. If it reaches the basements of nearby homes and other underground structures, mounded water tables can be problematic (Carleton, 2010). It is thus important to gain a better understanding of the degree to which mounding can occur under the ROW bioswales being installed in the thousands across New York City. This study uses the EPA Storm Water Management Model (SWMM5) as well as Visual MODFLOW 4.0 to model the hydrologic performance of an actual

15 5 constructed ROW bioswale installed at the intersection of Carroll Street and Denton place, County of Kings, in Brooklyn, New York. SWMM predictions were used to evaluate the stormwater retention capacity of the ROW bioswale. Visual MODFLOW model was used to simulate the groundwater mounds beneath it. ii. Literature Review Studies were conducted by others to evaluate the performance of Green Infrastructure and Low impact development (LID) by utilizing SWMM. William Groves (1999) and his bioswales group used the SWMM model to simulate the hydrologic processes of stormwater runoff for Camino Real project for analysis of bioswale efficiency for treatment of surface runoff. J. Sin and his team (2013) utilized SWMM 5 model to estimate the effect of LID facilities applied to Gimcheon Pyeonghwa district in Korea. Hua-Peng Qin (2013) in his study chose SWMM5 model to evaluate the effects of LID on urban flood reduction in an urbanizing area of Shenzhen, China. In each of these studies, local rainfall and/or observed surface flow rates are used to calibrate the respective SWMM models, which are then used to make inferences about how these facilities work. However, none of these previous studies focus specifically on ROW bioswales installed in a urban residential areas. Only a few studies were conducted with Visual MODFLOW to assess impact of the groundwater mounds that resulting from Green Infrastructure. Glen B Carleton in 2010 developed a finite difference groundwater flow model

16 6 with Visual MODFLOW to quantitatively predict the height and extent of groundwater mounding beneath and adjacent to engineered storm water infiltration structures during variable recharge events. He used his model to identify those aquifer characteristics (e.g. soil permeability, aquifer thickness, and specific yield) with the greatest potential effect on raising the local water table. Simulations were performed for m 2 and 40,470 m 2 development sites. The infiltration basins modeled were of a 0.15 m an 0.6 m depths, designs that differ significantly from the standard ROW bioswales being used in New York. By contrast, this analysis focuses on ROW bioswales installed in a small scale urban residential area in New York City. Actual infiltration rates were estimated using field monitoring results obtained with sixteen pressure transducers. Darcy s Law was applied to the piezometric head observations to determine variability of actual infiltration rate with time. The field monitoring effort was used to calibrate a SWMM model that can generate an infiltration time series, for multiple rain events, that were then used as input to Visual MODFLOW model. Finally, Visual MODFLOW was used to predict the height and extent of groundwater mounding. Figure 2 illustrates schematic overview of the SWMM and Visual MODFLOW model setup process in this study. This integrated modeling exercise will help to improve understanding of the functionality and impact of ROW bioswales.

17 Figure 2: A schematic overview of the SWMM and Visual MODFLOW model setup process 7

18 8 METHODS i. Site Description The subject ROW bioswale is located at the intersection of Carroll Street and Denton place, County of King in Brooklyn, New York (Figure 3). The project site is primarily comprised of paved roads and sidewalks. Most of the tributary area is impervious. The project site consists of approximately m 2 drainage area that is bounded by Carroll Street on the north and 1st Street on the south. According to available topography of the site, the ground surface varies from 5.91 m MSL (Mean Sea Level) along the southern property of street to 5.45 m MSL in northern portion of street. The topography of this area slopes gently towards the north at an overall gradient of 0.5 percent. Based on a boring test report performed by Stantec Consulting Services, the soil beneath the paved surfaces is characterized as sandy loam. The boring report claims that a water table was not encountered during the soil boring program conducted at the site. To be conservative, it is assumed that the water table is just below the bottom of the boring, at a depth of 6.09 m below the ground surface. Figure 3: Location of ROW bioswale and the Carroll Street Catchment Area (Google 2014)

19 9 ii. The Standard Design of ROW Bioswale A Type 1 NYCDEP bioswale was installed on the northeast corner of Denton Place. Plan and section details of the Type 1 Bioswale design are shown Figure 4 and Figure 5, respectively. It is 6.09 m long, by 1.52 m wide, with a depth of approximately 1.52 m. In section, a ROW bioswale consists of vegetation surfaced, a soil mixture layer and a stone drainage layer. The vegetated surface, planted with trees, shrubs and grasses, not only provides temporary surface storage for run off, but also filters out particulates and associated pollutants. The sandy soil is 50.8 mm deep, and underlain by 76.2 mm of gravel, allowing gradual infiltration of captured runoff. Type 1 bioswales are generally constructed without an underdrain system, meaning that all of the infiltrated water eventually percolates into the underlying in-situ soils. At street level, ROW bioswales also have an inlet and an outlet, cut into the curb. During rain events, storm water runoff drains into the bioswale through its inlet. When the ROW bioswale fills to its capacity, excess water flows out bioswale through the outlet which is located at its down gradient end, in this case close to the pre-existing catch basin that previously drained this entire side of the street. It was noted that during larger rain events, stormwater can bypass the inlet and outlet, after which it flows into the catch-basin.

20 10 Figure 4.Standard design drawings for bioswale type one (NYCDEP, 2013) Figure 5: Cross section view of bioswale type one (NYCDEP, 2013)

21 11 iii. Monitoring Equipment Sixteen Campbell Scientific CS451 Pressure Transducers were installed in the bioswale to measure piezometric head (Figure 7). The bottoms of eight pressure transducers were installed 0.91 m below the ground surface, and are referred to as shallow piezometers in this project. The other eight pressure transducers were installed to a depth of 1.52 m below the ground surface, and are labeled deep piezometers (Figure 6). The shallow piezometers measure piezometric head in the engineered soil of the bioswale. The deep piezometers extend into the undisturbed soil below the bottom of the bioswale. Piezometric head measurements were made every five minutes for a monitoring period that began on 7/26/2013 and is ongoing. The data was logged on a Campbell CR1000 data logger. All piezometric data was relayed to the Sustainable Water Resource Engineering Lab (SWRE) server located at Drexel University using cellular modems, for real time viewing by the study team. Two Texas electronics TE525 tipping bucket rain gauges were also installed on the rooftop of an adjacent building just south of the ROW bioswale. Rainfall was measured at five minute intervals during 7/26/2014 and 5/20/2014, and was logged on a Campbell CR1000 data logger. These two datasets (piezometric head measurements and rainfall) were used in the creation and calibration of the hydraulic models used in the study.

22 12 Figure 6: Schematic Cross Section View of Piezometers in Bioswale Figure 7: Schematic Layout of Piezometers in Bioswale

23 13 iv. Calculations of Observed Infiltration Rates The infiltration rate is a measure of the rate at which water moves through soil at the soil-air interface, and is commonly expressed in units of depth over time (ASTM). Through the process of infiltration, the fluid filling the voids in a porous medium is driven downwards by the pressure imposed by the overlying fluid. The gradient in piezometric head across the porous media into which the water is entering is a major determinant of the actual observed downward flow of water. Since the vertical piezometric head gradient across the bioswale can be evaluated using the pressure transducers, Darcy s Law was used to compute the actual time variable infiltration rate observed in the bioswale. Darcy s law is used extensively in groundwater studies. Formulated by a French engineer Henry Darcy in 1856, it describes the flow of a fluid through a porous medium (Darcy.H, 1856). The basic equation used in Darcy s law is as follows. In which q is the flow rate passing through the soil, L is thickness of the soil layer, and h1 and h2 is shallow and deep piezometric head respectably. In the ROW bioswale design, the engineered soil layer was intended to promote infiltration of bioswale run on and direct precipitation into the gravel layer, which stores the infiltrated water until it is able to percolate into the

24 14 underlying in-situ soils. Because the actual rate of downward flow of water in the bioswale is controlled by the rate at which the water can flow through the soil, its saturated hydraulic conductivity (K), and not that of the gravel, was used in the Darcy s Law calculations. In this study, the hydraulic conductivity (K) of the sandy loam engineered soil is assumed as 106 mm/hour according to SWMM design manual. The total head (h) is the sum of the piezometric head ( ) and the elevation head (z), as follows: h z (2) As mentioned above, shallow and deep piezometric head were measured directly by pressure transducers installed in the shallow and deep layers of the bioswale. To obtain a spatially averaged infiltration rate across the ROW bioswale, an average shallow ( 2 ) and deep ( 1 ) piezometric head was computed using the eight shallow and deep pressure transducer readings, respectively. The average values were used in the Darcy s Law computations. To obtain the elevation head, a datum was set at the elevation of the bottom of the deep pressure transducers, such that their elevation head ( z 1 ) was zero. The elevation head of the shallow pressure transducers, ( z 2 ) was set at 1.22 m, to reflect the vertical distance of their bottom above the deep transducers (Figure 8).

25 15 With these substitutions, Darcy s law is rewritten as 2 z2 K (3) L q 1 Figure 8: Schematic diagram of Darcy s Law where q represents the downward rate of flow into the subsurface under the bioswale (e.g. the bioswale s infiltration rate). The following assumptions were also made in this application of Darcy s Law: The direction of water flow was assumed to occur vertically downward through the soil and gravel layers Storm water infiltration was assumed to occur as a uniform wetting front

26 16 When water ponds on the surface of the bioswale above an elevation of 0.15 m, water can preferentially flow through two pipes into the underlying soil layer, bypassing the engineered soil. When the any deep piezometer reading was less than meters, it was assumed to be zero. Soil of uniform heterogeneity i.e. Ks is same throughout bioswale. v. Modeling Platforms: SWWM Model a) Instruction of SWMM model The EPA Storm Water Management Model (SWMM) is one of the most popular tools to simulate rainfall-runoff events in urban areas. It was developed by the EPA in 1971 and has been continually maintained and updated. The current version, version 5, is used in this research project. SWMM 5 tool can be an effective tool for quantifying the impact of Low impact development controls (LID) in controlling urban runoff (A.Rossman, 2011). A new feature of SWMM 5 enables modelers to simulate the hydrologic performance of low impact development (LID) controls such as bio-retention cells, vegetative swales, rain barrels, and infiltration trenches. This updated version also helps users to accurately represent specific types of green infrastructure in particular to determine their effectiveness. The infiltration estimates obtained from the pressure transducer observations, and associated Darcy s Law computations, were used to calibrate a

27 17 SWMM model of the bioswale in its particular streetscape configuration. After calibration, the model was used to perform an event-based analysis of stormwater reduction effectiveness during a hypothetical one inch (25.4 mm) storm event. The model was also used to perform continuous simulations using hourly precipitation data. This particular year has historically been used by the NYCDEP for facility planning purposes, since it is considered an average rainfall year. b) SWMM Model Development USEPA s SWMM model was used to model hydrologic and hydraulic response of the existing conditions within Carroll Street. In SWMM, the drainage area of Carroll Street is represented through four sub-catchments which are connected to one another in a configuration that mirrors actual field conditions (Figure 9). To model greened conditions, a low impact development (LID) control was deployed into sub-catchment 2 to represent the ROW bioswale. Subcatchment 2 receives both direct rainfall and runoff from other three catchment

28 18 areas Figure 9: The system layout consists of four sub-catchment areas S1 through S4. The system discharges to catch basin the point labeled Outfall1. All the initial parameters were estimated from physical information derived from either site investigations or the suggested value from the tables in the SWMM manual (A.Rossman, 2011). The percentage of impervious area in each sub-catchment was assumed to be 98%.The Green-Ampt method was used to model infiltration. Based on the geotechnical reports, groundwater was not observed down to 6.09 m during the subsurface investigations and assumed that groundwater was about 6.09 m below surface in this area. Therefore, the models assumed an aquifer was at a similar depth to estimate deep infiltration. A low impact development (LID) control was deployed into subcatchment 2 to represent ROW bioswale. In SWMM, the LID Controls feature is designed to simulate the green infrastructure response of the stormwater runoff conditions. LID controls can be deployed throughout a study area to evaporate,

29 19 store, and infiltrate sub-catchment runoff. The generic types of LID controls included in SWMM are bio-retention cells, porous pavements, infiltration trenches, rain barrels, and vegetative swales (Rossman, 2010). In this project, the bio-retention type of LID control was chosen to represent the ROW bioswale, due to its physical similarity to the actual facility. c) SWMM Model Calibration Model calibration is the process of refining model input parameters to achieve an acceptable degree of correspondence between predicted and observed results (ASTM, 1994). Observed field data from piezometer sensors was used to calibrate the SWMM model. Calibration of SWMM model was based on four different rain events, a mm (9/12/2013), a mm (9/21/2013), 7.37 mm (3/12/2014) and a mm event (3/29/2014). A sensitivity analysis on the uncalibrated model revealed that the infiltration rates simulated by SWMM are very sensitive to the values specified for soil hydraulic conductivity, porosity and field capacity. These values were thus adjusted in the calibration process. As stated earlier, other parameter values were specified based on the recommended values from the SWMM manual, surveyed physical conditions, and engineering judgment. These values were held fixed during the calibration and included the storage depth, thickness of soil layer, and height of storage layer. The calibration process involved repeated model runs using manually adjusted parameters in the LID Control editor. The

30 20 predicted infiltration rates obtained by SWMM were compared to the Darcy s Law computations, until the best fit values were obtained. The averages of the best fit values of all these parameters for the four calibration rain events were considered the final calibrated values. d) Validation of SWMM model method Model validation is the process of comparing predictions made with the calibrated model to another set of actual observations; to ensure that reasonable predictions have been obtained by the model. The validation of the SWMM model included comparison of the modelled and observed rates of infiltration as well as cumulative infiltration amounts for each validation event. Four rain events, 61.21mm (9/12/2013), mm (9/21/2013), mm (3/12/2014) and mm (3/29/2014) were used to validate SWMM model. The modelled infiltration was obtained directly from the SWMM output. Cumulative infiltration amounts were obtained by integrating the infiltration rates over time. e) Modeled Stormwater Retention The validated SWMM model was used to perform event-based and continuous simulations. Event-based analyses included comparison of stormwater volume retained (volume of infiltration) for one inch (25.4mm) 1 hour, 8 hour and 24 rain events (Figure 10). A comparison between predevelopment (without bioswale) and post-development (with bioswale)

31 21 conditions was performed for the one inch, 8 hour storm, constructed using a NRCS (Natural Resources Conservation Service) Type III distribution. The continuous simulations used 1988 precipitation from John F. Kennedy International Airport (Figure 11). This time series is used routinely by the NYC DEP for facility planning purposes due to its representativeness compared to historical precipitation in the city. Box plots were produced to compare the capture efficiency of ROW bioswale over 1988.

32 22 Figure 10:Time series of rainfall intensities for 1inch 1 hour, 8 hours and 24 hours. Figure 11: Time series of rainfall intensities for 1988 f) Hydraulic Loading Ratios (HLR) Analysis To ensure that the results of this study will be transferable to other locations where the same bioswale is hydraulically connected to larger or smaller tributary areas, a sensitivity analysis on the results considering different hydraulic loading ratios (HLRs) was performed. Specifically, the stormwater capture performance of the same facility subjected to the 1988 rainfall record was computed for tributary areas that were 25 to 125 times the area of the bioswale. The hydraulic loading ratio describes the ratio of impervious drainage area to infiltration area hydraulic loading ratio. Drainage area includes all the tributary drainage to the infiltration BMP and infiltration area includes the base area of the

33 23 BMP designed to infiltrate. The base area of bioswale is infiltration area. Hydraulic loading ratio can be expressed as follow: HLR= Drainage Area / Infiltration Area A larger HLR value corresponds to a larger drainage area when infiltration area is fixed. In this study, the infiltration area (the bioswale area) is 1.85 m²; and drainage area is 708 m², thus the hydraulic loading ratio is 76. To increase or decrease HLR value; the drainage area parameter in the SWMM model can be adjusted. To perform HLR analysis, 1988 rain events are applied to SWMM model. Box plot for a variety of HLR range from 25 to 125 HLR was created to evaluate the performance of the bioswale under changes in stormwater runoff quantity. vi. Visual MODFLOW Model Visual MODFLOW is the industry standard computer program for 3D groundwater flow modeling. Developed by Schlumberger Water Services and based on MODFLOW 2000, Visual MODFLOW has the ability to simulate threedimensional ground-water flow through a porous medium using a finitedifference method. It can deliver high-quality, three-dimensional visualization of groundwater model results. In this study, three dimensional groundwater flow model was developed using the Visual MODFLOW to simulate the height and

34 24 extent of groundwater mounds beneath ROW bioswale with various depth of rain events. a) Spatial Discretization and Boundary Condition The Visual MODFLOW model developed for the area including the Carroll Street ROW bioswale extends approximately 89 feet (27.43 m) in an eastwest direction and 48 feet (19.2 m) in a north-west direction. The model consists of 35 rows and 42 columns, and total 1470 cells with average size of 0.76 m x 0.6 m. A single layer with 25 feet (6.09 m) thickness is use to model the unsaturated zone (Figure 12). Figure 12: Model configuration of Carroll Street. The bioswale is representation by 1.52m x 6.1 m cells.

35 25 In Visual MODFLOW, the body of water (aquifer) is normally represented as a constant head boundary condition. The thickness of the unsaturated aquifer can be used to describe the depth of the water table (1.52 m). For simulations, the boundary head and initial head are set to 1.52 m (Figure 13). The water table is flat and aquifer thickness is constant at the start of the simulation. Figure 13:Cross-section View of Visual MODFLOW Model b) Hydraulic Conductivity and Recharge Visual MODFLOW requires specification of hydraulic conductivity and storage values for each grid cell to calculate interlayer leakage values (inflow into the soil). The vector hydraulic conductivity (K) represents the resistance to flow provided by the porous media. By contrast, the specific yield and total porosity parameters control how much water can drain from, and be stored in, the unsaturated zone, respectively. To simplify the model, the aquifer is assumed to be isotropic and homogeneous and hydraulic conductivity values are recommended from the design manual.

36 26 The ROW bioswale in Visual MODFLOW was modeled indirectly by assigning a large hydraulic conductivity into the 1.52 m x 6.1 m grid cells (Figure 12). According to the site boring report, the engineered media in the bioswale is sandy loam which has suggested conductivity value of 0.26 m per day. Visual MODFLOW s recharge is used to simulate infiltration of water from the bioswale into the underlying aquifer system. A sensitivity analysis performed on the Visual MODFLOW model suggested that predictions of groundwater table variability are most sensitive to the specified recharge rate, since it determines the total volume of storm water that enters the aquifer. A recharge rate was thus assigned to only those cells that represent the bioswale area. No recharge was allowed elsewhere, since the entire adjacent street and sidewalk surfaces are impervious. The bioswale recharge rates were generated with the calibrated SWMM model, and input as time series. The Visual MODFLOW simulations are thus transient, and can include changes in observed infiltration throughout actual rainfall events. Since Visual MODFLOW requires specification of many different parameters, a host of other assumptions were made to simply the model: The aquifer is assumed to be isotropic and homogeneous No slope is assumed to exist on the bioswale surface Infiltration is assumed to occur in a strictly vertical direction

37 27 The initial ground water table is assumed to be flat, and no other recharge is assumed to occur outside the bioswale. Though necessary, these simplifications and assumptions are a known source of uncertainty and imprecision in the modeled results. c) Visual MODFLOW Model Simulations Visual MODFLOW was used to simulate water table dynamics for both individual events, and for continuous simulations. For both sets of simulations, the recharge rates specified in Visual MODFLOW were derived by the SWMM model. The simulated groundwater mounding was then used to evaluate groundwater impact to nearby utility mains or other infrastructure under the ROW bioswale.

38 28 RESULTS i. On Site Monitoring Result For calibration and validation purposes, seven storm events (Table 1) between July 2013 and May 2014 were selected for analyses. These particular storm events were selected based on their depth and seasonal distribution within the observation period. Table 1: Event rainfall and duration Date Rainfall (mm) infall Duration (hours) note 9/12/ Calibration 9/21/ Calibration 3/12/ Calibration 3/29/ Calibration 11/26/ Validation 12/29/ Validation 4/30/ Validation Figure 14 show an example of pressure transducers reading (raw data) for piezometric head in 4/30/2014. It included eight shallow piezometers and eight deep piezometers.

39 29 Figure 14: Pressure transducers reading (raw data) for piezometric head in 4/30/2014 The observed piezometer head response (e.g. the average of the shallow and deep pressure transducer readings) from each of the seven events is shown in Figure 15, along with the associated hyetographs. Rain (mm)

40 30 Rain (mm) Rain (mm) Rain (mm)

41 31 Rain (mm) Rain (mm) Rain (mm) Figure 15: Observed the Piezometer Head of 9/21/2013, 9/12/2013, 11/26/2013, 12/29/2013, 3/12/2014, 3/29/2014 and 4/30/2014.

42 32 ii. Result of Darcy s Law Analysis Figure 16 shows the computed vertical infiltrated rate over time for each of the seven rain events, as obtained from Darcy s Law. Figure 16: Infiltration Rate Was Calculated with Darcy s Law

43 33 It is noteworthy that for all events, the infiltration rates obtained were of the same order of magnitude (between 11 and 15 mm/hr), suggesting that there is a maximum capacity at which water can flow into the underlying soil, perhaps driven by the design, and specifically depression storage capacity. iii. SWMM Model Result a) Result of Calibration Figure 17 lists the best fit parameter values for the calibrated model. The calibration process involved repeated model runs using manually adjusted parameters in the LID Control editor. The predicted infiltration rates obtained by SWMM were compared to the Darcy s Law computations, until the best fit values were obtained. The averages of the best fit values of all these parameters for the four calibration rain events were considered the final calibrated values. Figure 17: The average of optimized values of parameters from four rain events. Calibration focused on adjustment to hydraulic conductivity and porosity value, field capacity, and vegetation volume fraction.

44 34 b) Result of validation of SWMM model method The validation results are shown in Figure 18. Rain (mm) Rain (mm) Rain (mm) Figure 18: Comparison of Observed Infiltration Rates and SWMM Infiltration Rate

45 35 In most cases, SWMM slightly under predicted the instantantaneous infiltration rate computed using Darcy s law Figure 19, however, shows the predicted cumulative infiltration obtained from the analysis of the seven different storms, versus the observed values. The percent difference ranges from 5% to 10%, representing the uncertainty that must be attributed to the simulated values. Figure 19: Comparison of cumulative infiltration from SWMM 5 and Observed b) SWMM model result for event based SWMM model results for one inch (25.4mm), 1 hour, 8 hours and 24 hours rain events are illustrated in Figure 20. The result shows ROW bioswale can retain 1.04 m³, 1.24 m³ and 2.46 m³ of stormwater for 1 hour, 8 hours and 24

46 36 hours respectively. Also shown in this graph is DEP s estimate of the volume of infiltration that occurs from one bioswale during an average rain event. The larger capture rate for the longer storms is likely due to lesser bypass due to lower precipitation intensity, resulting in more infiltration. Figure 20: Stormwater Volume Retained by the ROW bioswale for one inch, 1 hour, 8 hours and 24 hours rain events A comparison between pre-development (without bioswale) and postdevelopment (with bioswale) conditions for the one inch, 8 hour storm is showed in Figure 21 and table 2. The percentage difference total volume of runoff is 6.87% between green and pre-development condition.

47 37 Figure 21: A comparison between pre-development (without bioswale) and postdevelopment (with bioswale) conditions for the 1inch, 8 hour storm. Table 2: A comparison between pre-development and post-development for I inch, 8hour storm Total Volume of Runoff (m³) Peak flow(m³/s) Pre-development Condition Post-development Condition Percent Difference 6.87% 10% c) SWMM Model Continuous Simulation (1988) Figure 22 graphically presents the results of the continuous modelling of the bioswale s performance during Separate lines depict cumulative runoff with and without the bioswale in place, as well as the cumulative volume of infiltration obtained through the bioswale. Table 3 summarizes these results.

48 38 Figure 22: Comparison of pro-development and post-development condition for cumulative runoff in Table 3: Comparison of pro-development and post-development condition for cumulative runoff in 1988 Cumulative Runoff in 1988 (m³) Pre-development Condition Post-development Condition Percentage Difference 9.48% The percent of runoff retained for each event occurring during 1988 is plotted in Figure 23. It reveals that the ROW bioswale can capture a higher percentage of smaller rain events, as expected.

49 39 Figure 23: Stormwater Volume Retained by the ROW bioswale for 57 rain events in 1988 The box plot shown in Figure 24 graphically depicts the percentage of three general categories of rain events that is infiltrated. For rain events less than one inch, the bioswale retained median of 17 percent of total runoff volume, with a 95% confidence range varying from 0.5% to 55%. However, for rain events greater than one inch (25.4 mm) but less than two inches (50.8 mm), the bioswale only retained an median of 7 percent of total runoff volume with a 95% confidence range spanning from 8% to 17%. For events larger than two inches (50.8 mm), the bioswale retained a median of 5 percent of total runoff volume with a 95% confidence interval varying from 7% to 16 %.

50 40. Figure 24: Stormwater Volume Retained by the ROW bioswale for one inch, 1 hour, 8 hours and 24 hours rain events Table 4: Summary for one inch, 1 hour, 8 hours and 24 hours rain events Rainfall Median Maximum Minimum Mean Below 1 inch (25.4 mm) 17% 55% 0.50% 26% 1-2 inch (25.4mm-50.8mm) 7% 17% 8% 6% Above 2 inch (50.8mm) 5% 16% 7% 5% Figure 25 graphically depicts the volume (m³) of stormwater infiltrated by the ROW bioswale for each of the 57 rain events that occurred in According to the DEP, each bioswale has been designed to capture an average of 2,244 gallons (8.5 m³) of water per rain event. Figure 25 demonstrates that the volume of water that SWMM predicts can be retained for each rain events in 1988 is significantly less than this value.

51 41 Figure 25: Stormwater Volume Retained by the ROW bioswale for one inch, 1 hour, 8 hours and 24 hours rain events (Plot box and summery table) d) HLR Test Results The box plot shown in Figure 26 depicts the variability in the percent of each event that retained from this bioswale from a range of different potential HLR values using the 1988 precipitation data. Note that the actual bioswale studied in this thesis has an HLR value of 76. Figure 26: Box and whisker plot comparing R% (Percent of Tributary Area Runoff Retained per event) from bioswales under of a variety of HLR for 1988 precipitation data.

52 42 As expected, the bioswale generally is more efficient at capturing runoff if it is less intensively loaded. However, beyond a certain HLR, the bioswale is essentially capturing nearly its maximum amount. Figure 27, shows that at the higher HLR values, infiltration starts sooner (due to more rapid concentration of catchment runoff), and continues longer and to greater cumulative values, due to a greater excess rainfall release time associated with the larger tributary areas. Figure 27: Cumulative infiltration versus time for different HLR values iv. Visual MODFLOW Results a) Events-Based Results Figure 28, Figure 29 and Figure 30 present the output from the Visual MODFLOW model for 1 inch 1 hour, 1 inch 8 hours, 1 inch 24 hours storms, respectively. The maximum heights of the groundwater table for the 1 inch 1 hour, 1 inch 8 hour and 1 inch 24 hour storms are, respectively, 0.15 m, 0.67 m and 0.76 m. The results reveal that the maximum height of groundwater

53 43 mounding is higher when for longer duration storms, consistent with the statement made earlier about greater cumulative infiltration and less bypass associated with lower intensity rains. Figure 28: Demonstrate surface water elevation contour (top) and height of groundwater mound (bottom) elevation for 1 inch, 1 hours rain events

54 44 Figure 29: Demonstrate surface water elevation contour (top) and height of groundwater mound (bottom) elevation for one inch, 8 hours rain events Figure 30: Demonstrate surface water elevation contour (top) and height of groundwater mound (bottom) elevation for one inch, 24 hours rain events

55 45 b) MODFLOW Continuous Simulation The box plot in Figure 31 depicts the simulated maximum height of groundwater mounding at several fixed distance from the edge of the bioswale for all storms during The maximum height of the simulated groundwater mounds occurs under the center of the bioswale (x=0 m). The median height of the mound under the center of the bioswale is 0.43 m, with a 95% confidence range extending from 1 to 1.10 m. The eastern (x=14 m) and western (x=-10.5 m) edges of elevated water table level are similar and less than 0.1 m above the assumed pre-storm water table elevation. Figure 31: Box and whisker plot comparing the height of ground water mounds of a variety of location across the street for 1988 precipitation. Note: the water table is assumed 6.1 m below the surface.

56 46 DISCUSSION Discussion of the Observations The monitoring (Figure 15) reveals that during wet weather the deeper piezometers almost always respond sooner than the shallower piezometers. Though this initial response is small and ephemeral, this observation suggests that some infiltration occurs near the inlet as soon as water enters the bioswale. This water infiltrates and spreads out in the higher permeability gravel layer, and begins to percolate into the underlying soils before the upper engineered soils have been saturated. This effect has not been simulated in the present research, but is expected to be a relatively minor amount, and unlikely to change the results significantly. The monitoring results also suggested that the bioswale can infiltrate up to a maximum amount of between 11 and 14 mm/hr (Figure 16). It appears that this maximum amount is determined by the maximum piezometric head difference that can be generated within the bioswale, given its design (and specifically the vertical distribution of depression, soil, gravel, etc). Through the process of infiltration, the fluid filling the voids in a porous medium is driven downwards by the pressure imposed by the overlying fluid. If there is a maximum head (driven by the designed maximum depression storage that can be sustained before bypass occurs), then it follows that there is a maximum rate of infiltration that cannot be exceeded. The vertical gradient in piezometric head

57 47 across the porous media thus appears to be a major determinant of the actual observed downward flow of water. If future bioswales are designed with greater depression storage depths, a greater volume of infiltration in each bioswale can be expected. This observation represents a key finding of the present work. Discussion of SWMM Modelling Results The SWMM results indicate that this ROW bioswale can retain higher volumes of stormwater for longer duration events, of identical total precipitation amounts. Figure 20 shows ROW bioswale would retain 1.04 m³, 1.24 m³ and 2.46 m³ of stormwater for one inch 1 hour, 8 hours and 24 hours design rain event respectively. If the one inch of precipitation is distributed over 24 hours, more than three times as much runoff is infiltrated, compared to a situation whereby the same depth of precipitation occurred over only one hour. As noted above, the bioswale has a certain capacity for storing storm water. During longer duration rain events, the bioswale takes a longer time to reach its maximum infiltration capacity. During this time, infiltration is ongoing, and void space volume is liberated to receive more stormwater. This observation suggests that by reducing the rate of inflow to a bioswale, for example by routing runoff first into a small subgrade cistern, the infiltration capacity of each bioswale may be further extended, another key design consideration warranting additional research. According to the Bioswales Care Hand Book provide by NYCDPED, each bioswale can collect an average of 2,244 gallons of water per rain storm!

58 48 However, Figure 25 illustrates that the stormwater volume (m³) retained by this bioswale during all events occurring in 1988 is far less than this value. It is unclear how the value cited in the Bioswales Care Hand Book was developed. If this value is being used in estimating the stormwater capture facility of all ROW bioswales installed in New York City, the discrepancy between the values simulated by the present study and the official value should be given greater attention. It is possible that the DEP value assumed a different bioswale design than that found at the present site. More research is necessary to investigate the maximum volume of infiltration obtained from bioswales with different siting and design configurations. Discussion of Sensitivity Analysis on the HLR The HLR analysis suggests that this bioswale could capture a higher percentage of tributary runoff per event if it were connected to a smaller drainage area. Over range of HLR values analyzed (Figure 26); the lowest HLR (25) yielded the highest retention. To note is the fact that at greater HLRs, the same bioswale infiltrates a greater volume of water. This observation is due to the hydraulic factors described previously. However, the higher HLR suggests that the bioswale has a greater tributary catchment area that generates more runoff. There is a negligible difference in capture efficiency at higher HLR, since the ratio of infiltration to runoff appears to even out. This result suggests both