Jul 21, 2005 - Lake Association (GLA) cell was flooded once with dry initial conditions. A value of zero ...... Rain Gardens: A How-To Manual for Homeowners.
IMPACT OF INPUT DATA QUALITY ON STORMWATER HYDRAULIC MODELING
By REID CHRISTIANSON Bachelor of Science in Biological and Agricultural Engineering Kansas State University Manhattan, Kansas 2001 Master of Science in Biological and Agricultural Engineering Kansas State University Manhattan, Kansas 2003
Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the Degree of DOCTOR OF PHILOSOPHY May, 2012
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IMPACT OF INPUT DATA QUALITY ON STORMWATER HYDRAULIC MODELING
Dissertation Approved: Dr. Glenn O. Brown Dissertation Adviser Dr. Daniel E. Storm
Dr. Garey A. Fox
Dr. Chad J. Penn Outside Committee Member Dr. Sheryl A. Tucker Dean of the Graduate College
TABLE OF CONTENTS Chapter
Page
CHAPTER I ..................................................................................................................................... 1 GENERAL INTRODUCTION, OBJECTIVES, AND CONCLUSIONS ....................................... 1 Objectives .................................................................................................................................... 6 Chapters as Manuscripts .............................................................................................................. 7 General Conclusions .................................................................................................................... 7 Land Use and Contributing Area ............................................................................................. 8 Soil Properties .......................................................................................................................... 8 Practical applications ............................................................................................................. 10 Future Work ........................................................................................................................... 11 References ...................................................................................................................................... 12 CHAPTER II.................................................................................................................................. 17 ACCURACY OF CURVE NUMBER ESTIMATION ON DISTURBED AND UNDISTURBED SOILS ............................................................................................................................................ 17 Abstract .......................................................................................................................................... 17 Introduction .................................................................................................................................... 18 Methods ......................................................................................................................................... 20 Theory ............................................................................................................................................ 23
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Chapter
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Infiltration .................................................................................................................................. 23 Curve Number Development ..................................................................................................... 26 Results............................................................................................................................................ 28 Effective Saturated Hydraulic Conductivity .............................................................................. 28 Sorptivity ................................................................................................................................... 31 Curve Number............................................................................................................................ 33 Conclusion ..................................................................................................................................... 40 References ...................................................................................................................................... 40 CHAPTER III ................................................................................................................................ 47 DEVELOPMENT OF A BIORETENTION CELL MODEL AND EVALUATION OF INPUT SPECIFICITY ON MODEL ACCURACY ................................................................................... 47 Abstract .......................................................................................................................................... 47 Introduction .................................................................................................................................... 48 Methods ......................................................................................................................................... 50 Model Description ..................................................................................................................... 50 Pilot-Scale Testing ..................................................................................................................... 54 Model Validation/Testing .......................................................................................................... 55 Results............................................................................................................................................ 58 Experimental Results ................................................................................................................. 58 Modeled Results ........................................................................................................................ 59
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Chapter
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Conclusions .................................................................................................................................... 70 Acknowledgements .................................................................................................................... 71 References ...................................................................................................................................... 71 CHAPTER IV ................................................................................................................................ 77 MODELING FIELD-SCALE BIORETENTION CELLS WITH HETEROGENEOUS INFILTRATION MEDIA .............................................................................................................. 77 Abstract .......................................................................................................................................... 77 Introduction .................................................................................................................................... 78 Methods ......................................................................................................................................... 80 Field Description........................................................................................................................ 80 Grove High School ................................................................................................................ 81 Grand Lake Association ......................................................................................................... 82 Field Testing .............................................................................................................................. 83 Model Description ..................................................................................................................... 84 Model Validation/Testing .......................................................................................................... 86 Results and Discussion .................................................................................................................. 87 Experimental Results ................................................................................................................. 87 Modeled Results ........................................................................................................................ 89 Conclusion ..................................................................................................................................... 99 Acknowledgements .................................................................................................................. 100
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References .................................................................................................................................... 100 APPENDIX A .............................................................................................................................. 104 APPENDIX B .............................................................................................................................. 168
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LIST OF TABLES Table
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Table 2.1. Double ring infiltrometer measurement locations in Kansas and associated soil type. Measurements at these 15 locations were taken between 2006 and 2008. The area weighted soil survey based curve numbers account for multiple soil types (multiple hydrologic soil groups) spanning a given site. ..................................................................................................................... 21 Table 2.2. Mean and median saturated hydraulic conductivities
from double ring
infiltrometer tests at 15 sites in Kansas. The 15 sites were separated into 5 land use categories. Engineered indicates that native soils were removed from the site an replaced with an engineered soil, Urban altered indicates the sites are associated with urban development such as commercial, or residential land uses, Rural altered was around a farm house, Prairie had been undisturbed for more than 20 years, and Rural Unaltered was agricultural land. ................................................... 30 Table 2.3. Difference between measured saturated hydraulic conductivity ( estimated
) and soil survey
. Difference between Curve Number (CN) estimates from soil survey soils data
and best matching land cover (Method 3- Soil Survey) and the mean and median values for Method 1, which related and sorptivity (
to CN, and the mean and median of Method 2, which related ) to CN. Each mean and median, and soil survey value is weighted based
on the number of observations at a site relative to the total number of samples in the site land use classification. ................................................................................................................................. 35
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Table
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Table 2.4. Curve Number (CN) estimates based on saturated hydraulic conductivity ( sorptivity (
) measurements (Method 1 -
, Method 2 -
) and
) from Chong and Teng (1986)
(Equations 2.10 and 2.11 page 27), estimates based on soil survey soils data (Method 3 - Soil Survey), and estimates based on observed runoff data (Method 4 - Runoff). The Johnson County Transit (JCT) site had three land cover phases with the majority of measurements taken during the residential phase. ...................................................................................................................... 36 Table 3.1. Bioretention cell model input values for the soil mixes from pilot-scale bioretention cell scenarios with increasing levels in input specificity (level 1 being the least specific). Scenarios 1.1, 1.2, and 1.3 represent the un-mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. Scenarios 2.1, 2.2, and 2.3 represent the mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. .......................................................................................................................... 56 Table 3.2. Input values for development of saturated hydraulic conductivity of macropores (
). The parameters
,
, and
, are the macropore aerial porosity,
number of pore size classes, and the largest macropore radius, respectively. Scenarios 1.1, 1.2, and 1.3 represent the un-mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. Scenarios 2.1, 2.2, and 2.3 represent the mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. ... 57 Table 3.3. Experimental results for pilot-scale bioretention cell measured parameters for each scenario. Scenarios 1.1, 1.2, and 1.3 represent the un-mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. Scenarios 2.1, 2.2, and 2.3 represent the mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. ................................................................................................................. 58
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Table
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Table 4.1. Soil hydraulic parameters as model input for the primary (upper layer) infiltration media from each flooded bioretention cell in Grove, Oklahoma. The original model uses area weighted soil hydraulic properties to account for sand plugs in the cells. The revised model considers the sand plugs separately. The parameters ,
,
, and
are the porosity,
suction head at the wetting front, saturated hydraulic conductivity, and field capacity, respectively. Hydraulic parameters were taken from Saxton and Rawls (2006). .......................... 87 Table 4.2. Measured and modeled parameters for two artificially flooded bioretention cells in Grove, Oklahoma. The Grove High School site was flooded twice with the tests representing initially dry conditions as well as initially wet conditions. The Grand Lake Association was flooded with initially dry conditions. Total outflow values may not equal overflow total plus drain total due to rounding. ..................................................................................................................... 90 Table 4.3. Model difference for two bioretention cell models (original and revised) compared to observed [(modeled-observed)/observed] for three artificial flooding events in Grove, OK. The Grove High School cell was flooded twice – the first flooding event represented dry initial soil conditions in the cell and the second flooding event represented wet initial conditions. The Grand Lake Association (GLA) cell was flooded once with dry initial conditions. A value of zero would indicate model results exactly matched observations. Negative values indicate modeled values are lower than observed. The original model used weighted soil hydraulic properties while the revised model used distinct properties to account for sand plugs in the bioretention cells. .......... 91
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LIST OF FIGURES Figure
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Figure 2.1. Measured effective saturated hydraulic conductivities at fifteen locations in Kansas. The Engineered land use category consisted of a complete removal of native soil and replacement with an engineered infiltration media. The top and bottom of the box indicates the 75th and 25th percentiles, respectively, and the solid line contained in the box is the median value. The dotted line indicates the arithmetic mean. The top and bottom of the whiskers indicate the 90th and 10th percentiles, respectively. Soil survey data points were area weighted based on soil classifications at each site. ..................................................................................................................................... 29 Figure 2.2. Calculated sorptivity at the monitored locations in Kansas. The Engineered land use category consisted of a complete removal of native soil and replacement with an engineered infiltration media. The top and bottom of the box indicates the 75th and 25th percentiles, respectively, and the solid line contained in the box is the median value. The dotted line indicates the arithmetic mean. The top and bottom of the whiskers indicate the 90th and 10th percentiles, respectively. ................................................................................................................................... 32
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Figure
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Figure 2.3. United States Department of Agriculture - Natural Resource Conservation Service CN values calculated for 15 sites in Kansas from observed saturated hydraulic conductivity only (Method 1 and
) and observed saturated hydraulic conductivity and sorptivity (Method 2 ). The top and bottom of the box indicates the 75th and 25th percentiles,
respectively, and the solid line contained in the box is the median value. The dotted line indicates the arithmetic mean. The top and bottom of the whiskers indicate the 90th and 10th percentiles, respectively. For the Johnson County Transit site, the Method 3 CN of the primary land use (residential 2000 m2 lots) was used for comparison as the majority of infiltration tests were done with this land use. .......................................................................................................................... 34 Figure 2.4. Comparison of measured runoff calculated Curve Numbers (CN) from two sites in Kansas and the Chong and Teng (1986) CN saturated hydraulic conductivity (
) relationship.
The box and whisker plots show the range of estimated CN values for runoff (horizontal boxes) and
estimated (vertical boxes). The top (and right) and bottom (and left) of the box
indicates the 75th and 25th percentiles, respectively, and the solid line contained in the box is the median value. The dotted line indicates the arithmetic mean. The top and bottom of the whiskers indicate the 90th and 10th percentiles, respectively. A 1:1 relationship shown with solid black line. Data displayed for the Johnson County Transit site is for the “residential” phase of the study. ....................................................................................................................................................... 38
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Figure
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Figure 2.5. Correlation between Method 3 - Soil Survey estimates of CN at sites that were heavily disturbed or altered (Engineered and Urban Altered sites) and at sites where less disturbance occurred (Rural Altered, Prairie, and Rural Unaltered) and Method 1 -
(Chong and Teng,
1986) estimated CN. A positive correlation existed between Method 3 and Method 1 when considering the less disturbed sites (R2 of 0.0934 and a RMSE of 7.52) while a negative correlation existed between Method 3 and Method 1 when considering the highly disturbed sites (R2 of 0.00103 and a RMSE of 22.1). ............................................................................................ 39 Figure 3.1. Hydraulic processes considered in the bioretention cell component model initially created for the Integrated Design, Evaluation, and Assessment of Loadings (IDEAL) model. .... 50 Figure 3.2. Cross section of pilot-scale bioretention cells used to collect observed data for comparison to bioretention cell model results. Tests were run with and without mulch. .............. 54 Figure 3.3. Comparison of modeled results from four input specificity levels compared to pilotscale bioretention cell tests for drainage volume. The ^ represents no model result due to no drain flow. Scenarios 1.1, 1.2, and 1.3 represent the un-mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. Scenarios 2.1, 2.2, and 2.3 represent the mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. ................................................................................................................. 60 Figure 3.4. Comparison of model results from four input specificity levels compared to pilot-scale bioretention cell tests for maximum drain flowrate. The ^ indicates the model predicted no water would leave through the drain in 25 hours. Scenarios 1.1, 1.2, and 1.3 represent the un-mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. Scenarios 2.1, 2.2, and 2.3 represent the mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand....................................................................... 62
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Figure
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Figure 3.5. Comparison of model results from four input specificity levels compared to pilot-scale bioretention cell tests for the time of maximum drainage. The ^ indicates the model predicted no drainage would occur during the 25 hour simulation. Scenarios 1.1, 1.2, and 1.3 represent the unmulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. Scenarios 2.1, 2.2, and 2.3 represent the mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand...................................... 64 Figure 3.6. Comparison of model results from four input specificity levels compared to pilot-scale bioretention cell tests for drain start time. The ^ indicates the model predicted water would not start to drain in the 25 hour model simulation. Scenarios 1.1, 1.2, and 1.3 represent the unmulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. Scenarios 2.1, 2.2, and 2.3 represent the mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand...................................... 65 Figure 3.7. Comparison of model results from four input specificity levels compared to pilot-scale bioretention cell tests for drawdown time. The ^ indicates the model predicted water would be ponded longer than 25 hours. Scenarios 1.1, 1.2, and 1.3 represent the un-mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand. Scenarios 2.1, 2.2, and 2.3 represent the mulched tests with 25%, 50%, and 75% Greenville, SC soil with the balance made up of construction sand....................................................................... 67 Figure 3.8. Comparison of ponded water depth above the surface of the pilot-scale bioretention cell infiltration media (soil mix or mulch layer) for the fitted model (specificity level four) and observed data. Scenarios 1.2 and 1.3 represent the un-mulched tests with 50% and 75% Greenville, SC soil with the balance made up of construction sand. Scenario 2.3 represents the mulched test 75% Greenville, SC soil with the balance made up of construction sand. ............... 69
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Figure
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Figure 4.1. Generalized schematic of the bioretention cells in Grove, OK. The two bioretention cells artificially flooded were the cells at Grove High School and the Grand Lake Association. Each cell had sand plugs to convey large inflows through to the underdrains. The Grove High School cell had two underdrains with eight sand plugs while the Grand Lake Association cell had three underdrains and six sand plugs. ............................................................................................ 81 Figure 4.2. Overflow and drainage at the Grove High School bioretention cell in Grove, OK with initially dry soil conditions. Overflow was calculated by using manual depth measurements and a broad crested weir equation (a). Subsurface drain flow was measured by collecting a timed volume of water (b). Results from two bioretention cell models are also displayed. For both the original and revised models the measured inflow hydrograph was used with an initial soil moisture (0.11 m3/m3). The original model used weighted soil hydraulic properties while the revised model used distinct properties to account for sand plugs in the bioretention cells. .......... 94 Figure 4.3. Overflow and drainage at the Grove High School bioretention cell in Grove, OK with initially wet soil conditions. Overflow was calculated by using manual depth measurements and a broad crested weir equation (a). Subsurface drain flow was measured by collecting a timed volume of water (b). Results from two bioretention cell models are also displayed. For both the original and revised models the measured inflow hydrograph was used with an initial soil moisture (0.14 m3/m3). The original model used weighted soil hydraulic properties while the revised model used distinct properties to account for sand plugs in the bioretention cells. .......... 95
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Figure
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Figure 4.4. Overflow and drainage at the Grand Lake Association bioretention cell in Grove, OK with initially dry soil conditions. Overflow was calculated by using automated depth measurements and a broad crested weir equation (a). Subsurface drain flow was measured by collecting a timed volume of water (b). Results from two bioretention cell models are also displayed. For both the original and revised models the measured inflow hydrograph was used with an initial soil moisture (0.11 m3/m3). The original model used weighted soil hydraulic properties while the revised model used distinct properties to account for sand plugs in the bioretention cells. ........................................................................................................................... 96 Figure 4.5. Infiltration media moisture at 0.150 to 0.300 m under the sand plugs (a) and under the soil (b) and at 0.600 to 0.900 m depth under the sand plugs (c) and under the soil (d) for the Grove High School bioretention cell in Grove, OK with dry initial soil conditions. Values were normalized based on the largest and smallest observed measurements because the probes were not calibrated. Predictions from two bioretention cell models is also shown. The original model used weighted soil hydraulic properties while the revised model used distinct properties to account for sand plugs in the bioretention cell. ................................................................................................ 98
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CHAPTER I
GENERAL INTRODUCTION, OBJECTIVES, AND CONCLUSIONS
The U.S. Environmental Protection Agency (EPA) currently regulates point source pollution from municipal separate storm sewer systems (MS4s) through the National Pollutant Discharge Elimination System (NPDES). In general, each state is responsible for the administration of the NPDES program. This legislative approach aims to reduce water pollution by requiring municipalities to regulate quality and quantity of stormwater runoff. Indeed, most stormwater quality improvement projects have been initiated because of the political nature of environmental protection. The Federal Water Pollution Control Act of 1948 was augmented by the Clean Water Act (CWA) in 1977 (USEPA, 2012) and gave the U.S. EPA considerable control over the nation’s water quality standards (Muskie, 1978; Novotny, 2003; Novotny and Olem, 1994). This has had a substantial impact on industrial point source pollution under the CWA NPDES, which is due to the comparatively easy-to-identify source of this type of water pollution. However, with significant gains made in the improvement of water quality impacted by point sources, diffuse or non-point source (NPS) pollution is now considered a major water quality issue (USEPA, 1996; USEPA, 2002). Once urban diffuse pollution reaches a municipal stormwater system it is considered a point source and is subject to the requirements of the municipalities NPDES permit. Certainly the diffuse nature of urban runoff pollution requires
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many parties and stakeholders to mitigate negative water quality impacts. However, the permit holder is ultimately responsible and municipalities holding permits generally adopt ordinances regulating post-construction runoff quality and quantity. The EPA is currently proposing to broaden the stormwater rules by expanding the applicable areas covered by the rule and establishing a common set of required minimum measures for all MS4s (USEPA, 2011), which could substantially increase the number of sites requiring post-construction runoff quality and quantity controls. In urban areas, the increased amount of impervious area (or simply less pervious area) compared to previous pre-development woodlands, prairies or agricultural lands leads to hydrologic impacts on local streams including increased runoff volume and peak flow rates (Whipple et al., 1983). Increased runoff quantity has a tendency to cause flash flooding (Hollis 1975) and increased peak flowrates provide more energy for waters to erode stream banks (Shaver, et al. 2007; Whipple et al., 1983). Neller (1998) and Caraco (2000) both found a watershed can be negatively impacted with impervious areas of as little as 14% of the land surface area. The extent of negative impacts have been addressed by Dietz and Clausen (2008) and Waananen (1969) who showed an increase in annual runoff, and Jennings and Jarnagin (2002) who showed increases in stream flow with increasing amounts of impervious surfaces. Changes in watershed hydrology have an impact on stream channel stability and have been shown to be strong predictors for aquatic ecosystem health (Kennen et al., 2010; Townsend et al., 1997). Additionally, a study done by Trimble (1997) indicated urban streams can have up to two-thirds of the sediment load coming from the erosion of stream banks. Whereas in agricultural areas, stream bank erosion only consists of up to one-fifth the load (Collins et al., 1997: Walling and Woodward, 1995). Major pollutants impacting urban runoff water quality consist of sediment, nutrients, chlorides, trace metals, petroleum hydrocarbons, microbial pollution, and organic chemicals 2
(Shaver, et al. 2007). There are several sources for these pollutants such as construction sites, parking lots, roads, and yards. Stormwater control practices are intended to mitigate the effect of pollution by trapping pollutants, which are stored and sometimes degraded over time. Additionally, some stormwater control practices such as erosion control mats and sedimentation basins are intended to reduce the erosive effects of runoff by preventing erosion or limiting peak flowrates. Although there are many ways to address urban stormwater pollution, there are eight tools listed by the Center for Watershed Protection (CWP, 1998) that encompass the basics. These tools include land use planning, land conservation, aquatic buffers, better site design, erosion and sediment control, stormwater best management practices (BMPs), non-stormwater discharges, and watershed stewardship programs. There are overlaps between these tools, but the CWP (1998) suggests land use planning be the first step. However, after the period for land use planning has passed, other options to meet NPDES permit requirements include structural BMPs like wetlands, vegetative filter strips, dry ponds, and bioretention cells (Jeer, 1997). Ponds are the most common stormwater control structure used to collect and detain runoff. These structures are designed to limit the peak flowrate of a two-year storm to at or below predevelopment levels (Finkenbine et al., 2000). Similarly, wetlands can also be designed for flood control (USEPA, 2006). This structural detention approach has worked to some degree; however, the number of bank full flows may increase due to smaller storms producing the same peak flows although with a shorter duration (Leopold, 1994). Increased occurrences of bank full flow could increase the erosive force exerted on the stream by more than a factor of three predevelopment conditions (Leopold, 1994). Larger ponds with lower peak flows can be built to mitigate the increased bank full flows (Maryland Department of the Environment (MDE), 2000).
3
Bioretention cells, BRCs, are one relatively new option to address stormwater quality and quantity. These systems are affectionately known as rain gardens in many neighborhood areas. Bioretention cells, like ponds, are designed to collect runoff and are a possible solution to help meet permit terms for urban flood reduction and pollutant removal (Jeer, 1997; CWP, 1998; PGDER, 1993) and to help meet NPDES requirements. In general, BRCs only receive runoff from small areas – maybe up to one hectare – where ponds can be used to mitigate runoff from much larger areas. Vegetation type and infiltration media vary, but soil is usually sandy to allow for adequate infiltration (Davis et al., 2001). Since these systems are typically small, the design takes into account pollution from the first flush event, which contains the majority of constituents. Schueler (1994) states many communities define the first flush as the first half inch of runoff – roughly associated with 90% of the pollutants – and Novotny (2003) defines it as the first 40% of runoff delivering 60% of pollutants. Infiltration practices like BRCs have been shown to reduce runoff from small storms, and when used in a distributed manner across a watershed, can substantially reduce annual runoff (Barr Engineering Company, 2004; Brander et al., 2004). While BRCs are a novel stormwater treatment option providing multifunctional benefits, the feasibility of accurately modeling bioretention cells (BRCs) for stormwater control needs to be further explored. The ability to accurately estimate the impact of BRC mitigation will save both time and money during the planning and implementation phase of a water quality project. However, few tools are available to accurately estimate the physical processes affecting bioretention cell performance. For example, while there are several reports of annual average pollutant removal from BRCs (Davis et al., 2001; Hunt et al., 2006; PGCMD, 2009), there are relatively few studies of pollutant removal rate based on system hydraulics (Davis, 2008; Davis et al., 2003). Since stormwater control requires land area, costs associated with implementation can be substantial, and city officials may be hesitant about investing in a structure with only vague
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information about performance. The use of reliable modeling tools gives designers and regulators confidence that money spent on these systems will be a sound investment. In order to develop more optimized BRC designs, soil physical properties must be known for the possible area of practice implementation as well as for the surrounding area contributing runoff to the practice. Currently, many designers and engineers collect soil data (e.g., saturated hydraulic conductivity) for potential sites from the Soil Survey Geographic (SSURGO) database. This data source is easily accessed and widely used in a geographic information systems (GIS) framework (Fox and Miller, 2003). However, these data and other more recently developed, but very limited, soils databases (i.e. the USDA National Soil Information System, NASIS) (USDA, 2009) may not necessarily represent the site (NRCS, 2007; Seelig, 1993) due to alterations of the soil structure via land use changes, cut-fill operations, and compaction (Gregory et al., 2006). Site specific data can be collected in situ with infiltrometers, which can be used to collect some of the most important design data (saturated hydraulic conductivity). When used in conjunction with soil bulk density and soil type, accurate designs can be made. In addition to estimation methods for soil physical parameters affecting stormwater BMPs, there are several methods to estimate runoff from the BMP contributing area. The most basic approach is using the rational method with a triangular hydrograph. However the NRCS TR-55 unit hydrograph approach with the USDA Natural Resources Conservation Service (NRCS) Curve Number (CN) (NRCS 1986) is commonly used (Woltemade, 2010). The CN method takes into account information on soil type and land use as well as precipitation and soil moisture in the form of antecedent runoff conditions (ARC). Intuitively, infiltration controls runoff meaning soils having higher infiltration rates result in less runoff and vice-versa. Considering this, Chong and Teng (1986) developed an empirical relationship between saturated hydraulic conductivity and potential water retention which can be
5
used to determine CN. Chong and Teng (1986) expanded the potential water retention relationship developed by incorporating sorptivity, which is a partial function of soil moisture, as initial infiltration rates are likely to be higher when the soil is dry. Due to the cost of a trial-and-error approach to installing BRCs, there is a need to accurately model both potential runoff volumes given soil and land use characteristics as well as water movement through multi-layered BRCs. In any such models, however, there is uncertainty about the level of specificity of the input parameters. In other words, to accurately model BRC water movement, is the use of literature derived soil parameters sufficient, or rather, do more specific laboratory or field-determined soil parameters need to be used? Or if soil heterogeneity is present in a BRC, can average soil characteristics be used or is it beneficial to use the specific characteristics of each soil? Likewise, is the prevalent use of the CN method for determining runoff delivered to a BMP based on SSURGO soils data specific enough to provide an adequate starting point? OBJECTIVES
To improve stormwater runoff and BRC modeling efforts, this Ph.D. dissertation work evaluated three primary objectives. Each objective is addressed in a separate chapter.
The first objective is addressed in Chapter II and intended to provide increased understanding of the accuracy of runoff CN estimates.
The second objective is addressed in Chapter III and aimed to develop and test, in a pilot-scale setting, a one dimensional BRC model with differing levels of input specificity.
The third objective is addressed in Chapter IV and aspired to validate the one dimensional BRC model from Chapter III at the field-scale and compare predictions to a revised model.
6
CHAPTERS AS MANUSCRIPTS
Chapter II: Accuracy of Curve Number Estimation on Disturbed and Undisturbed Soils. The co-authors for this work are Dr. Stacy Hutchinson from the Department of Biological and Agricultural Engineering at Kansas State University and Dr. Glenn Brown from the Department of Biosystems and Agricultural Engineering at Oklahoma State University. As the primary author of this paper, Christianson analyzed these data, compared methods, and wrote the content with advice on direction and content from co-authors. Support for this work was provided by Stacy Hutchinson. Chapter III: Development of a Bioretention Cell Model and Evaluation of Input Specificity on Model Accuracy. The contributors of this work were Reid Christianson, Glenn Brown, Bill Barfield, and John Hayes. As the primary author of this paper, Christianson collected and analyzed these data, compared methods and wrote the content with substantial advice on direction, content, and funding coming from co-authors. Chapter IV: Modeling Field-Scale Bioretention Cells with Heterogeneous Infiltration Media. The contributors were Reid Christianson, Glenn Brown, Rebecca Chavez, and Daniel Storm. As the primary author of this paper, Christianson analyzed these data, compared methods and wrote the content with advice on direction, substantial help with data collection, as well as funding coming from co-authors. GENERAL CONCLUSIONS
Reducing urban NPS pollution from stormwater is a key environmental issue in the United States. Because stormwater runoff flow volume and flow rates are important determinants of the potential of these waters to cause environmental harm, modeling these process correctly both prior to water arriving at a BMP and within potential BMPs is crucial to the improvement of water quality. Overall it was clear that the easiest model input values to obtain (e.g., SURGGO values) were sufficient for areas with less disturbed soils (non-urban) or homogenized infiltration
7
medias. Unfortunately, homogenous and undistributed conditions are rarely the case in urban situations where land use changes and development operations can alter the soil physical characteristics of the BMP contributing area and where the multifunctionality of the BMP may necessitate multiple media types. In these situations, field specific information is often needed to adequately model the hydrologic processes involved in stormwater routing. Land Use and Contributing Area
When designing stormwater control systems for contributing areas with undisturbed soils, using standard soil survey values for soil physical properties and for runoff curve number calculation will likely provide adequate results and allow for consistent designs. However, this work showed that in areas with disturbed or altered soils, the NRCS curve number may be better predicted with in situ saturated hydraulic conductivity tests. Due to the positively skewed nature of saturated hydraulic conductivity observations from double ring infiltrometer tests, the median is generally a better statistic to describe the central tendency of the property. In practice, using soil survey values to estimate runoff volume with the NRCS curve number would likely cause undersizing of a stormwater facility in urban areas due to underestimation of the design flow. The unknown level of compaction and cut/fill operations in residential, commercial, and industrial landscapes leaves uncertainty about hydraulic process modeling which may negatively influence the design of stormwater control BMPs. Because improperly sized BMPs can cause more damage than no BMP implementation, it may be best to use site-specific, field-measured information in the design of stormwater BMPS for urban areas. Soil Properties
Once the delivery of stormwater to a BMP is correctly estimated, the routing of water though the BMP is the next critical hydraulic process. Input required for this stage of modeling involves the BMP structure parameters such as size and infiltration media characteristics. While the former is an easily measured parameter, the characteristics of the infiltration media such as saturated hydraulic conductivity and bulk density require more effort to estimate. Methods of
8
obtaining these physical characteristics including using professional judgment, using published reports of soil types to reference these properties, or performing site-specific field measurements which can be resource intensive. For stormwater BMP modeling applications, more accurate input information for a site may result in more accurate model results. As there are few tools available for modeling the hydraulic processes in a BRC, this work included development of a BRC model which was tested against results from a set of pilot-scale BRCs. Several sources of input with increasing levels of specificity were used with this model in order to determine the accuracy of model input required to obtain model results close to observations. Based on model structure, saturated hydraulic conductivity was the most important parameter when evaluating performance. Additionally, the more specific the input hydraulic parameters were to the particular infiltration media tested, the more accurate model results were. The fitting parameter in this effort was a macropore adjustment, which changed the effective saturated hydraulic conductivity used in the model. Since no information about macropores was known, this parameter was only used to bring model results into agreement with observations. The fitting parameter approach yielded the best results. Although the fitted model was not necessarily an obtainable goal for professionals in the field or practicing stormwater engineers (information about macropores is rarely available), the results showed high model performance in this research application. Relative to the fitted model, estimating hydraulic parameters based on soil texture (i.e., sand, silt, and clay content) and bulk density, was the next most specific set of input data. Both soil texture and bulk density can easily be estimated with field sampling and would be the dataset most likely obtainable or available to a designing engineer. Model results using this soils information and bulk density were not as close to observations as the fitted model, though would likely be suitable for designing overflow and underdrain structures of a BRC. Finally, basing model results upon information available from a soil map yielded the poorest results. 9
Modeling events in a controlled environment is the first step in assessing the applicability of a given model and the feasibility of collecting or obtaining adequate input data. The next step is field-scale model validation to determine if modeled processes are adequate to represent the heterogeneity and other complicating factors influencing field studies. To assess the field applicability of the model, the performance of two BRCs in Grove, Oklahoma containing sand plugs was monitored and compared to model results. Due to poor model performance in estimating the hydraulic processes of the heterogeneous infiltration media in the Grove BRCs, revisions were integrated into the model. The addition of these revisions allowed for more accurate simulation of drainage parameters as well as overflow timing. With these revisions, the overflow rate and volume were overestimated likely due to horizontal seepage through the edges of the BRCs tested which was not accounted for in the model. Overestimating the overflow rate could lead to conservative designs for spillway layout; this would increase overall cost of the BRC, but would allow the system to be less prone to failure. Similar to pilot-scale testing, using more system specific input information yielded more accurate modeling results. However, when only vague input information is available (e.g., general soil type), using a model designed to incorporate specific input may not yield benefits. In other words, it is recommended that practicing stormwater professionals use models with levels of complexity that appropriately match specificity of available input data. Moreover, model selection should be based on the goals of the project and input data should be collected accordingly. A designer must know the types of storms the BMP will receive runoff from, including size, and shape of hydrograph. Inflow information is the primary input to all hydrologic process models, and as such, time should be spent developing this component. Practical applications
In order to determine the level of modeling detail needed, the user must know the end purpose of the results. A city engineer, for example, would likely only be interested in a simple model similar to the original model described in Chapter 3 as they may only be investigating the 10
sufficiency of a given proposed design. A consulting engineer designing bioretention cells, however, would likely be interested in a more complex model like the revised model described in Chapter 4 because it provides additional capability to model enhanced or specialized systems that may be designed to remove a specific pollutant or suite of pollutants. Overall, it can be concluded that a model should be used that will meet the goals of the project. Additionally, if appropriate input data is not available, a complex model should not be used. Future Work
Efforts should be made to continue to validate the curve number calculation method in disturbed or altered areas. Moreover, with current advances in computing power, the capability is now available to more readily share information (e.g., via supplemental journal materials, etc.) meaning loss of data, such as the original CN information, can now be avoided. These new computing capabilities also can serve to enhance the collaborative nature of research, hopefully eventually resulting in more applicable and increased availability of site-specific data for stormwater professionals and design engineers. Specifically regarding BRCs, more event based research needs to be performed to allow for direct comparison with the results of popular computer models.
11
REFERENCES Barr Engineering Company. 2004. Burnsville Rainwater Gardens. Land and Water: The Magazine of Natural Resources Management and Restoration 48(5):47-51. Brander, K., K. Owen, and K. Potter. 2004. Modeled impacts of development type on runoff volume and infiltration performance. Journal of the American Water Resources Association 40:961-969. Caraco, D.S. 2000. The Dynamics of Urban Chanel Enlargement. Watershed Protection Techniques 3:729-734. Chong, S.K., and T.M. Teng. 1986. Relationship between the runoff curve number and hydrologic soil properties. Journal of Hydrology 84:1-7. Collins, A., D. Walling, and G. Leeks. 1997. Source type ascription for fluvial suspended sediment based on a quantitative composite fingerprinting technique. Catena 29:1-27. CWP. 1998. The Tools of Watershed Protection, p. 26, In D. Caraco, et al., (eds.) Rapid Watershed Planning Handbook: A Comprehensive Guide for Managing Urbanizing Watersheds. ed. The Center For Watershed Protection, Ellicott City, MD. Davis, A. 2008. Field performance of bioretention: Hydrology impacts. Journal of Hydrologic Engineering 13:90-95. Davis, A., M. Shokouhian, H. Sharma, and C. Minami. 2001. Laboratory study of biological retention for urban stormwater management. Water Environment Research 73:5-14. Davis, A., M. Shokouhian, H. Sharma, C. Minami, and D. Winogradoff. 2003. Water quality improvement through bioretention: Lead, copper, and zinc removal. Water Environment Research 75:73-82.
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Dietz, M., and J. Clausen. 2008. Stormwater runoff and export changes with development in a traditional and low impact subdivision. Journal of Environmental Management 87:560566. Finkenbine, J., J. Atwater, and D. Mavinic. 2000. Stream health after urbanization. Journal of the American Water Resources Association 36:1149-1160. Fox, H.D., and S.N. Miller. 2003. Walnut Gulch Experimental Watershed (WGEW) Database Support. p. 562-566 In K. G. Renard, et al. (eds.) Proc. First Interagency Conference on Research in the Watersheds, Benson, AZ2003. U.S. Department of Agriculture, Agricultural Research Service. Gregory, J., M. Dukes, P. Jones, and G. Miller. 2006. Effect of urban soil compaction on infiltration rate. Journal of Soil and Water Conservation 61:117-124. Hollis, G.E. 1975. The effect of urbanization on floods of different recurrence interval. Water Resources Research 11:431-435. Hunt, W., A. Jarrett, J. Smith, and L. Sharkey. 2006. Evaluating bioretention hydrology and nutrient removal at three field sites in North Carolina. Journal of Irrigation and Drainage Engineering-Asce 132:600-608. Jeer, S. 1997. Online resources for planners American Planning Association, Washington, DC. Jennings, D., and S. Jarnagin. 2002. Changes in anthropogenic impervious surfaces, precipitation and daily streamflow discharge: a historical perspective in a mid-atlantic subwatershed. Landscape Ecology 17:471-489.
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Kennen, J., K. Riva-Murray, and K. Beaulieu. 2010. Determining hydrologic factors that influence stream macroinvertebrate assemblages in the northeastern US. Ecohydrology 3:88-106. Leopold, L.B. 1994. A View of the River Harvard University Press, Cambridge, MA. MDE. 2000. 2000 Maryland Stormwater Design Manual. Maryland Department of the Environment, Baltimore, MD. Muskie, E.S. 1978. The Meaning of the 1977 Clean Water Act. EPA Journal 4. Neller, R. 1998. A comparison of channel erosion in small urban and rural catchments, Armindale, New South Wales. Earth Surface Processes and Landforms 13:1-7. Novotny, V. 2003. Water Quality Diffuse Pollution and Watershed Management. 2nd ed. John Wiley & Sons, Inc., New York, NY. Novotny, V., and H. Olem. 1994. Water Quality: Prevention, Identification, and Management of Diffuse Pollution John Wiley & Sons, Inc., New York, NY. NRCS. 1986. Urban Hydrology for Small Watersheds. Natural Resources Conservation Service, Washington, DC. NRCS. 2007. Hydrologic Soil Groups, p. 14 National Engineering Handbook, Part 630 Hydrology. ed. USDA-NRCS, Washington, DC. PGCMD. 2009. Bioretention Manual. Prince George's County, MD, Prince George's County, MD. PGDER. 1993. Design Manual for use of Bioretention in Stormwater Management. Prince George's County Department of Environmental Resources, Landover, MD.
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Schueler, T.R. 1994. First Flush of Stormwater Pollutants Investigated in Texas. Watershed Protection Techniques 1:88-89. Seelig, B. 1993. Soil Survey: The Foundation for Productive Natural Resource Management, In N. D. S. University, (ed.) Extension Bulletin, Vol. 60. North Dakota State University, Department of Agronomy, Fargo, ND. Shaver, E., R. Horner, J. Skupien, C. May, and G. Ridley. 2007. Fundamentals of urban runoff management: Technical and institutional issues USEPA. Townsend, C., S. Doledec, and M. Scarsbrook. 1997. Species traits in relation to temporal and spatial heterogeneity in streams: A test of habitat templet theory. Freshwater Biology 37:367-387. Trimble, S. 1997. Contribution of stream channel erosion to sediment yield from an urbanizing watershed. Science:1442-1444. USDA. 2009. National Soil Information System (NASIS) | NRCS Soils. Available at http://soils.usda.gov/technical/nasis/ (accessed July 24). USDA - NRCS. USEPA. 1996. Nonpoint Source Pollution: The Nation's Largest Water Quality Problem, Pointer No. 1. EPA 841-F-96-004A. USEPA, Washington, D.C. USEPA. 2002. National water quality inventory: 2000 Report. EPA 841-R-02-001. USEPA, Washington, D.C. USEPA. 2006. Stormwater Wetland. Available at http://cfpub.epa.gov/npdes/stormwater/menuofbmps/index.cfm?action=factsheet_results &view=specific&bmp=74 (accessed February 18 2012). U.S. Environmental Protection Agency.
15
USEPA. 2011. Proposed National Rulemaking to Strengthen the Stormwater Program. Available at http://cfpub.epa.gov/npdes/stormwater/rulemaking.cfm (accessed February 18th). U.S. Environmental Protection Agency. USEPA. 2012. Summary of the Clean Water Act. Available at http://www.epa.gov/lawsregs/laws/cwa.html (accessed 3-8-2012). USEPA. Waananen, A.O. 1969. Urban Effects on Water Yield, p. 169-182, In W. L. Moore and C. W. Morgan, (eds.) Effects of Watershed Changes on Streamflow. ed. University of Texas Press, Austin, TX. Walling, D.E., and J.C. Woodward. 1995. Tracing Sources of Suspended Sediment in River Basins - A case-Study of the River Culm, Devon, UK. Marine and Freshwater Research 46:327-336. Whipple, W., N.S. Grigg, T. Grizzard, C.W. Randall, R.P. Shubinski, and L.S. Tucker. 1983. Stormwater Management in Urbanizing Areas Prentice-Hall, Inc., Englewood Cliffs, NJ. Woltemade, C.J. 2010. Impact of Residential Soil Disturbance on Infiltration Rate and Stormwater Runoff1. Journal of the American Water Resources Association 46:700-711.
16
CHAPTER II
ACCURACY OF CURVE NUMBER ESTIMATION ON DISTURBED AND UNDISTURBED SOILS
ABSTRACT Estimates for the USDA-NRCS runoff Curve Number (CN) generally are based on a soil map and observed land cover; however, in areas where soil disturbance operations have occurred, the soil map may not be accurate. Instead a CN estimate based on a relationship to field measured effective saturated hydraulic conductivity,
, and sorptivity,
, from an infiltration test is an
effective alternative. The objective of this research was to determine if CN estimates using soil survey data are appropriate in areas with disturbed soils. A total of 331 double ring infiltration tests were conducted over the 15 sites included in this study. Sites were sorted based on land use and site history, into categories of engineered, urban altered, rural altered, prairie, and rural unaltered. Measured
values were skewed so the medians of these data were a better predictor
of central tendency. The prairie and rural unaltered median
values were closer to soil map
estimates than the other categories (between 0.0% and 91% from soil survey). Two empirical methods were used to develop CN estimates from infiltration data; Method 1 used only Method 2 used both
and
and
. Results from these two methods were not statistically different
at 12 of the 15 sites (α=0.05). When comparing these methods to CN values developed from soils
17
data and land cover (Method 3), better overall agreement existed between Method 1 and Method 3. The median CN value from Method 1 was the best predictor for the mean CN based on measured runoff data for an urban altered land use site (0.6% different), while Method 3 was the best predictor for the mean CN based on measured runoff data for a prairie land use (0.0% different).
INTRODUCTION With stormwater regulations compelling municipalities to control runoff quality and quantity of construction and post development areas, accurately estimating the hydrologic process is critical in developing adequate best management practice (BMP) designs. Additionally, models estimating the performance of certain BMPs are available to help guide designs. A common input for nearly every model is runoff water delivered to the BMP, and calculating this parameter is generally the starting point for BMP sizing (Hunt and White, 2001; PGCMD, 2009) and one of the most important components in the hydrologic process (Woltemade, 2010). Therefore, accuracy in estimation of runoff water helps to ensure effective BMP performance. Multiple methods for estimating runoff volume and peak flowrate are available, each with advantages and disadvantages. The most basic approach is the use of a triangular hydrograph where the peak flowrate is estimated using the rational method. While this approach is easy and likely suitable for rough designs, it may not accurately represent a true hydrograph. Additionally, a unit hydrograph can be developed for a given watershed using observed rainfall and runoff data or estimated with various models (Haan et al., 1994) if data are available. Although unit hydrographs are specific to a region, the United States Department of Agriculture - Natural Resources Conservation Service (USDA-NRCS) has developed a widely used generic unit hydrograph method called the NRCS TR-55 method which uses a runoff curve number (CN) to determine runoff volume and a pre-developed hydrograph distribution (USDA-NRCS, 1986).
18
Data for the development of the CN were collected in the 1930s and early 1940s using a variety of methods, including using “F” type sprinkle infiltrometers on plots that were 1.8 m wide by 3.7 m long (Mishra and Singh, 2003). Precipitation and a CN value based on land use and hydrologic soil group (HSG) are both required inputs for the method (Hawkins et al., 2009). Currently, many designers and engineers use readily available soils data (e.g., saturated hydraulic conductivity or HSG) for potential site designs from soil surveys (Woltemade, 2010), which make up the Soil Survey Geographic (SSURGO) database. This data source is easily accessed and widely used in a geographic information systems (GIS) framework (Fox and Miller, 2003). However, these data and other more recently developed, but very limited, soil databases (i.e. the USDA National Soil Information System, NASIS) (USDA, 2009) may not necessarily accurately represent a given site (USDA-NRCS, 2007; Seelig, 1993) due to alterations of the soil structure via land use changes, cut-fill operations, and compaction (Gregory et al., 2006). Although limited work has been done developing CNs for disturbed areas (Woltemade, 2010), a study in New Jersey (Friedman et al., 2001) investigated several land uses with various levels of compaction during construction and found all of the sites included in the study had a different NRCS HSG than would have been assumed based on SSURGO soil data. Additionally, newly disturbed or altered areas tend to have lower infiltration rates than established areas (Legg et al., 1996), which further complicates the use of generalized soils data in the development of CNs for a given site. If a site is disturbed, as is likely the case in urban areas, an alternative method for determining the saturated hydraulic conductivity would be to use infiltrometers. Though they require on-site effort, infiltrometers can be used to collect some of the most pertinent in situ data. Data from infiltrometers could even be used to simply adjust the HSG, as was done by Woltemade (2010) in a study on residential soil disturbance. Additionally, Woltemade (2010)
19
suggested the wide use of the CN method for runoff estimation in urban areas has led to a need for runoff studies on urban soils. The objectives of this work were to (1) collect effective saturated hydraulic conductivities (
) in the field and use the results to estimate a CN for 15 sites in Kansas and (2) compare
these CNs with CN estimates made from soil survey information and corresponding land cover.
METHODS All 15 study sites were located within the Great Plains (-94° 50' 42.0" to -96° 49' 26.4" longitude and 39° 12' 43.2" to 38° 51' 54" latitude) of the United States in the moist subtropical, mid-latitude climatic region (NOAA, 2011) (Table 2.1). Rainfall in this area ranged from 760 to 890 mm at the most westerly site to 890 to 1,015 at the site furthest east (National Atlas, 2011). The sites were categorized as either engineered (native soil completely removed and replaced with an engineered infiltration media), urban altered, rural altered, prairie, or rural unaltered. All 331 infiltration measurements at the 15 sites were collected between April 2006 and July 2008 using double ring infiltrometers (ASTM, 2006). Between 3 and 195 infiltration tests were performed at each site. Infiltration tests at the site with 195 observations were done in conjunction with several other studies. The primary resultant parameters from each infiltration tests was sorptivity ( ) and effective saturated hydraulic conductivity ( the final steady infiltration rate from the infiltration tests. These calculate CNs (Chong and Teng, 1986; Method 1 their corresponding
), which was assumed equal to values were used to
). Additionally, these
values and
values were used to calculate additional CNs for each site using a second
method which required the estimation of (Chong and Teng (1986); Method 2 -
using the initial portion of the infiltration curve and
). These CNs, calculated using both methods,
were then compared to CN estimates made from Soil Survey Geographic (SSURGO) soils data accessed from the Web Soil Survey (USDA, 2011) (Method 3 - Soil Survey). Curve numbers estimated using these soil survey data considered the hydrologic soil group (A, B, C, and D) 20
along with the appropriate land use at each location (Haan et al., 1994). For several sites, which spanned more than one soil group, an area weighted CN was developed. For validation of these CN estimates, runoff volume was measured at two sites (Ft. Riley Military Base and Johnson County Transit Authority) thus allowing direct CN calculation (Method 4 - Runoff).
Table 2.1. Double ring infiltrometer measurement locations in Kansas and associated soil type. Measurements at these 15 locations were taken between 2006 and 2008. The area weighted soil survey based curve numbers account for multiple soil types (multiple hydrologic soil groups) spanning a given site. Land Use **
Land Use Category
Soil Series
Hydrologic Soil Group
Area Weighted Soil Survey based Curve Number (Method 3)
Number of Infiltration Tests
1
Engineered
Pawnee clay loam
D
84
18
1
Engineered
Pawnee clay loam
D
84
9
Johnson County Transit*
1; 2; 3
Urban Altered
B, C
74 85† 75†
35‡
Campus - East
4
C
74
6
Riley County Multi-Family
4
D
80
4
Campus - Seaton
1
Smolan silt loam
C
79
3
Campus - Square
4
Smolan silt loam
C
74
4
Riley County Residential
4
Urban Altered
WymoreKennebec Complex
B, D
73
9
4
Urban Altered
Sogn-Vinland Complex
D
80
18
Site Name Jackson Street Stormwater Facility Hillcrest Stormwater Facility
Quinton Heights Stormwater Facility Rural Shawnee Grama Rural Shawnee Buffalo
Urban Altered Urban Altered Urban Altered Urban Altered
Chillicothe silt loam (65%); Oska-Martin Complex (35%) Chase silty clay loam Wymore silty clay loam
Martin silty clay C 74 6 loam Martin silty clay 4 C 74 6 loam Crete silty clay Ft. Riley Military loam (50%); 5 Prairie C, D 75 195 Base* Dwight-Irwin Complex (50%) Clime-Sogn Konza Prairie 5 Prairie C, D 76 6 Complex Rural Shawnee Rural Martin silty clay 5 C 71 6 Big Blue Unaltered loam Rural Shawnee Rural Martin silty clay 6 C 88 6 Fallow Unaltered loam 2 The ** indicates specific land use: 1 – Open spaces - fair condition; 2 – Residential - 2000 m lot; 3 – Cultivated land without conservation treatment; 4 – Open spaces - good condition; 5 – Meadow - good condition; 6 – Cultivated land - with conservation treatment. The † indicates that the CN was adjusted for impervious roof area. The * indicates a location where runoff was measured in addition to infiltration. The ‡ indicates that 24 infiltration tests were done during the residential phase and 11 were done during the open spaces - fair condition phase. 4
Rural Altered Rural Altered
21
The Jackson Street and Hillcrest Stormwater facilities were urban stormwater infiltration BMPs containing engineered soils composed of a mixture of compost and sand. Native soils were removed from the engineered sites and replaced with the engineered soil. At the Jackson Street site, the engineered soil was approximately 150 mm thick while at the Hillcrest Stormwater facility was amended to approximately 900 mm with an installed underdrain. The Johnson County Transit (JCT) location, Quinton Heights Stormwater Facility, Riley County sites, and all the campus sites were termed “urban altered” land use due to urban or commercial (university) development. Though the Quinton Heights facility was a dry pond for stormwater management, it was not considered an engineered site as the soils were native. The JCT site had three land use phases with the residential phase being the longest with the majority of the measurements. Although the JCT site was not a residential property, this land use was applied because the site had the characteristics of a residential property in terms of the proportion of impervious area to pervious area. The Rural Shawnee Grama and Buffalo sites were both located in the lawn of a country home (i.e. not in a development) and were designated “rural altered”. The other two Rural Shawnee sites, Big Blue and Fallow, were considered “rural unaltered” as these native soils had an agricultural use. The fallow site experienced conventional tillage, which, based on visual observation, was likely moldboard plowed and disked. Prairie sites included undisturbed locations on the 40,000 ha Ft. Riley Military Base and the Konza Prairie, a NSF affiliated LongTerm Ecological Research site. Although there were a total of 15 sites, only the Fort Riley and Johnson County Transit (JCT) sites had available runoff data. The work done at Fort Riley military base previously by Kim (2006), St. Clair (2007), Satchithanantham (2008), and Culbertson (2008), encompassed the majority of the infiltration tests with 195 individual data points. Additionally, runoff from rainfall simulations was measured for 118 events. This site was located in Riley County, Kansas with silty clay loams (Crete silty clay loam – Pachic Argiustolls: fine, smectitic, mesic; Dwight-Irwin
22
Complex – Typic Natrustolls: fine, smectitic, mesic and Pachic Argiustolls: fine, mixed, mesic) (USDA, 2011). Plot size was 60 m2 (3 m x 20 m) with plots separated by sheet metal. All runoff events occurred during the growing season at this site. The JCT site was described by Christianson et al. (2008) and Culbertson (2008) with the infiltration data partially presented by Culbertson (2008) and Hutchinson et al. (2011). Thirty-five infiltration tests were performed and 22 runoff events were measured at this site located in Johnson County, Kansas (Chillicothe silt loam – Oxyaquic Argiudolls: fine, smectitic, mesic; Oska-Martin Complex – Vertic/Aquertic Argiudolls: fine, smectitic, mesic) (USDA, 2011). This site had three land uses (Table 2.1) with eight runoff events during the residential phase, eight events during the bare soil phase, and six events during the prairie revegetation phase. The runoff contribution area of this site consisted of 1450 m2 of vegetated area and approximately 170 m2 of roof area. The watershed was delineated with sheet metal to limit runoff volume to the study area. Runoff events occurred during the growing and the dormant seasons. Additionally, this site was replanted from a traditional fescue lawn to prairie grasses in 2008 with bare or nearly bare soil until the end of May 2008. The residential land use phase had data from 24 infiltration tests collected and data from 11 infiltration tests in the prairie revegetation phase, although the results from the two phases were not significantly different (P=0.50). At both locations (Ft. Riley and JCT), runoff events were monitored by routing runoff water through a 90° v-notch weir with the depth of water over the weir monitored using a model 6712 ISCO and an associated 730 bubbler module.
THEORY INFILTRATION
Since infiltration is one of the primary indicators for runoff, infiltration experiments can be used to collect pertinent. Parameters from each infiltration test were sorptivity ( ) and
23
effective saturated hydraulic conductivity (
), which was assumed equal to the final steady
infiltration rate from the infiltration tests. Sorptivity is a measure of water absorption/desorption. Because
is a function of water
content, this parameter is important in the early stages of infiltration when the soil is relatively dry. Conceptually, this is consistent with the NRCS-CN method as CN values can be decreased for dry antecedent soil moisture and increased for wet conditions. An estimate of
was made
using the initial portion of each infiltration curve with a method proposed by Bach et al. (1986) based on the Philip infiltration equation (Philip, 1957). (2.1) where is cumulative infiltration, is time, and relates the final measured infiltration rate,
,
to the Philip parameter . Taking the derivative of Equation 2.1 with respect to time, allowed for analysis of infiltration rate at a given time and yielded:
(2.2)
where
is the infiltration rate. Graphing (
)
(
) allowed for the estimation of
using the slope of the best fit line (Bach et al., 1986). Parameter , which can range from 0-1, was used as a fitting parameter by minimizing the sum of the mean square error (MSE) between graphed (
) and the best fit predicted (
) of a trend line with a zero intercept
for each individual infiltration test (Bach et al., 1986). Many studies have used 0.33 for the parameter (Chong, 1983; Sharma et al., 1980), although in these studies, 0.33 was developed for the infiltration rate at one hour into the infiltration test. Work done comparing the Philips equation to the Green-Ampt equation suggests a higher value (0.67) would be more appropriate as
represents a steady state infiltration
rate (Youngs, 1968). Bach et al. (1986), who similarly used as a fitting parameter, found the 24
parameter ranged from 0.00 to 0.99, which was consistent with the findings of this study (0 to 1). Although the range was large, the mean value including all sites was 0.29 with a standard deviation of 0.39. In the present study, double ring infiltrometers were used with relatively deep ponded depths (up to 250 mm), thus, an alteration must be made when determining sorptivity. Through a derivation by (Philip, 1958) addressing an adjustment to soil surface, a relation between
(
where
and
for height of water ponded over the
could be made.
)
is ponded depth and
(2.3)
is the capillary potential or suction head at the wetting front.
(Talsma, 1969) added this adjustment to Philip’s original equation. Modifying Talsma’s equation by replacing the constant, which was 1/2.8, with the fitting parameter c results in:
(
)
(2.4)
Although more robust infiltration equations are available that account for ponded water on the soil surface (Haverkamp et al., 1990), they were not be used here as initial moisture content was not necessarily known and ponded depth was neither constant nor, necessarily, a function of time, which limited the use of these equations. It should also be noted
was not
adjusted for ponded depth as ponded depth was of limited importance at large times (greater than about 1 hour) (Lewis and Powers, 1938). The method proposed by Bach et al. (1986) was applied to Equation 2.4 leaving to the slope of the best fit line through (
chosen over that of (Collis-George, 1977) (
) [
25
([
]
(
)
equal
). This method was ]
) as it was empirical and
has no need for soil moisture information during the test. Additionally, the method proposed by (Smiles and Knight, 1976) (plotting
with slope equal to
) was not used as it did not
allow adjustment of ponded water over the soil surface. CURVE NUMBER DEVELOPMENT
The basic CN method is a very commonly used way to estimate runoff volume. At the two locations where measured runoff volume and area of the contributing watershed were available, these parameters were used to calculate an equivalent runoff depth which was equated to runoff from the NRCS-CN method (USDA-NRCS, 2004). (
where
)
(2.5)
is runoff volume,
is precipitation, and
is potential retention. Potential retention can
also be used to determine the CN (Mockus, 1972):
(2.6)
Combining Equation 2.5 and Equation 2.6 yields Equation 2.7 (Hawkins, 1973) as reported by Hawkins (1993), which was used to determine a specific runoff CN value using measured Q and P.
[
√ (
(2.7)
)]
Consistent with Chow et al. (1988), the derived CNs were adjusted to match antecedent runoff conditions (ARC) where initially dry conditions, and
represents normal moisture conditions,
represents
represents initially wet conditions.
(2.8)
(2.9) 26
Because the original infiltration data used to develop CNs along with the exact methods of incorporating infiltration data into the CN calculation have never been published and are assumed to be lost (Chen, 1981; Chong and Teng, 1986; Hawkins et al., 2005; Hawkins et al., 2009; Ponce and Hawkins, 1996), the original development methods cannot be used to relate infiltration data to CNs. Rather, a relationship between
,
, and potential retention ( )
(which is then used to calculate CN) developed by (Chong and Teng, 1986) was used. As opposed to the method proposed by Morel-Seytoux and Verdin (1983), which uses rainfall characteristics and estimates of interception and storage to develop a CN relationship, this method was used because rainfall, interception, and storage were not part of field measurements at the 15 sites investigated. The methods proposed by Chong and Teng (1986) were based on infiltration data from 87 locations in Hawaii, which was originally reported by Dangler et al. (1976) using 10 different soil types. The relationship between CN and
with this dataset had a coefficient of
determination (R2) of 0.77 and a root mean square error (RMSE) of 4.0 mm. Adjusting the Chong and Teng (1986) equation to reflect
in mm with
in mm/s in order to correspond with the
CN method and this infiltration study, resulted in: (2.10) The accuracy of the estimate for 3.2 mm) by adding and
was shown to improve (i.e. R2 of 0.85 and RMSE of
(Chong and Teng, 1986). Adjusting to reflect
in mm with
in mm/s0.5 yields: (2.11)
27
in mm/s,
RESULTS EFFECTIVE SATURATED HYDRAULIC CONDUCTIVITY
The range in effective saturated hydraulic conductivities from the engineered and urban altered areas was greater than from the other land uses (Figure 2.1). This indicated multiple observations would be required to appropriately estimate
with infiltrometers for site specific
design in altered areas. The need for large numbers of observations has also been reported by Pitt et al. (1999), Warrick and Nielsen (1980), and Woltemade (2010). Additionally, the mean value for these engineered and urban altered sites was typically higher than the median value, which indicated these data were positively skewed (i.e., most of the observations were lower than the mean) and has also been noted in other infiltration studies (Nielsen et al., 1973). In a general sense, positively skewed data indicates the median value may be a more appropriate statistic to use as a measure of central tendency than the mean (Snedecor, 1946), which, in this case, would apply to the measured
values.
28
Figure 2.1. Measured effective saturated hydraulic conductivities at fifteen locations in Kansas. The Engineered land use category consisted of a complete removal of native soil and replacement with an engineered infiltration media. The top and bottom of the box indicates the 75th and 25th percentiles, respectively, and the solid line contained in the box is the median value. The dotted line indicates the arithmetic mean. The top and bottom of the whiskers indicate the 90th and 10th percentiles, respectively. Soil survey data points were area weighted based on soil classifications at each site.
The average Coefficient of Variation (CV) for all 15 sites was 101%. This variation was larger than those found in other
studies; however, large CVs are common for the
parameter (Warrick and Nielsen, 1980). For example, Rogowski (1972) documented a CV for hydraulic conductivity between 5.3% and 68.0% and Gumaa (1978), as referenced by Warrick and Nielsen (1980), reported a CV for
of 190%. The large range indicated substantial
heterogeneity in the areas tested.
29
Urban areas in this study were all altered, meaning they had been graded or were adjacent to a building, which likely would cause compaction and possibly complete removal of the topsoil. This resulted in no consistent trend between soil survey
and mean or median infiltration
values for this land use (Table 2.2). The lack of a trend indicates the unpredictability of altered locations when using generalized databases for site characterization (soil surveys).
Table 2.2. Mean and median saturated hydraulic conductivities
from double ring infiltrometer tests at 15 sites in
Kansas. The 15 sites were separated into 5 land use categories. Engineered indicates that native soils were removed from the site an replaced with an engineered soil, Urban altered indicates the sites are associated with urban development such as commercial, or residential land uses, Rural altered was around a farm house, Prairie had been undisturbed for more than 20 years, and Rural Unaltered was agricultural land.
Median (% different from Soil Survey) mm/s
Coefficient of Variation for %
Soil Survey
Site
Land Use Category
Mean (% different from Soil Survey) mm/s
Jackson Street
Engineered
0.0235 (682)
0.00193 (-35.6)
255
0.0030
Hillcrest
Engineered
0.0324 (980)
0.0161 (437)
128
0.0030
JCT
Urban Altered
0.0146 (112)
0.00499 (-27.7)
130
0.0069
mm/s
EastA
Urban Altered
0.0327 (989)
0.0301 (902)
46.1
0.0030
Jardine
Urban Altered
0.0155 (415)
0.0113 (278)
83.9
0.0030
SeatonA
Urban Altered
0.0114 (27.0)
0.00731 (-18.8)
110
0.0090
QuadA
Urban Altered
0.00299 (-66.8)
0.00295 (-67.2)
57.1
0.0090
Hutch
Urban Altered
0.00447 (-15.4)
0.00394 (-25.6)
62.3
0.0053
Quinton
Urban Altered
0.0115 (27.9)
0.00471 (-47.7)
184
0.0090
Mark Grama
Rural Altered
0.00705 (135)
0.00475 (58.3)
122
0.0030
Mark Buffalo
Rural Altered
0.00635 (112)
0.00712 (137)
35.6
0.0030
Ft. Riley
Prairie
0.00875 (90.6)
0.00505 (10.1)
134
0.0046
Konza
Prairie
0.00730 (95.5)
0.00711 (90.6)
20.0
0.0037
Mark Big Blue
Rural Unaltered
0.00271 (-9.68)
0.00297 (-1.04)
62.5
0.0030
Mark Fallow
Rural Unaltered
0.00309 (2.98)
0.00309 (3.06)
81.6
0.0030
In contrast to the engineered and urban altered sites, the rural unaltered sites showed a very close match between observed values and the soil survey value. This trend was expected since the soil has not been altered. In a rural setting, disturbance or altered soils could be
30
problematic for BMP design, as illustrated by the two rural Shawnee altered sites. Although a complete site history was not known, these two datasets were each from a different side of a country home, thus a combination of heavy equipment and fill soil may have had an impact on these observed results. The measured
values at the least disturbed, most natural sites, the two prairie
locations, were unexpectedly different than the soil survey
. The Konza prairie range of
values was higher than the soil survey (95.5% and 90.6% different for the mean and median, respectively), though this may have been an artifact of the small recorded population size. Although the mean value was substantially higher than the soil survey value at the Ft. Riley site (90.6%), the observed median value was very close (10.1%). This difference was due to the highly positively skewed results at the Ft. Riley site. SORPTIVITY
Extensive active root systems work to dry the soil and can impact observed infiltration rates while slowing surface runoff (Linsley et al., 1949; Scott, 2000). The impact of dry soil can be observed when directly examining infiltration data with the calculation of
(Philip, 1957).
Additionally, vegetation can have an impact on soil structure by increasing organic matter and aggregation (Baver, 1940) and soil structure impacts
(Smettem, 2002). Thus, if double ring
infiltrometer tests are not performed for sufficient lengths of time to reach steady state conditions, the test can be dominated by the capillarity of the soil (Clothier, 2001) and an accurate estimate of cannot be made. Since
is partially a function of initial soil moisture, no significant trends in this
parameter were expected between sites (Figure 2.2) except when comparing data from the same date on the same soil. For example, the two rural unaltered sites (Big Bluestem and Fallow) each sampled on June 7, 2005 and November 3, 2005 with results showing the site with vegetation had a higher
. This meant the soil was likely drier under the Big Bluestem grass location when 31
compared to the fallow field. For comparison, Talsma (1969) found
to equal 3.98 mm/s0.5 with
a dry sand and 0.58 mm/s0.5 with a dry clay loam. Additionally, Zhang (1997) reports a range of 0.83 mm/s0.5 to 1.38 mm/s0.5 for
with various water contents in a loam soil.
Figure 2.2. Calculated sorptivity at the monitored locations in Kansas. The Engineered land use category consisted of a complete removal of native soil and replacement with an engineered infiltration media. The top and bottom of the box indicates the 75th and 25th percentiles, respectively, and the solid line contained in the box is the median value. The dotted line indicates the arithmetic mean. The top and bottom of the whiskers indicate the 90th and 10th percentiles, respectively.
As mentioned earlier, the fitting parameter can range from 0 to 1 and was used to fit a model to these observed data. The parameter values for individual sites ranged from 0 at the Riley County Multi-Family site to 0.96 at the Konza Prairie site. Low values indicate
had a larger
influence on infiltration rate during the infiltration tests. From Equation 2.4 (page 25) it was
32
apparent that a of 0 would indicate
was not important when determining infiltration rate or
total infiltration volume during the test and the test should have been run for longer. This seemed to be the case at the Riley County Multi-Family and the Campus - East sites, meaning the tests should have been extended to achieve reliable results. Examining the two rural unaltered sites, which were on the same soils, showed that when the
values were higher the values were lower. This same trend was observed in the two rural
altered sites as well, indicating the soil under the Big Blue (rural unaltered) site was likely drier than the soil under the Fallow (rural unaltered) site and the soil under the Buffalo (rural altered) site was likely drier than the soil under the Grama (rural altered) site. CURVE NUMBER
Because soil moisture, soil physical characteristics, and land use are all accounted for in the mathematical structure of a CN, the combination of
(a soil physical property) and
(based on initial moisture as well as soil properties) should, intuitively, represent CN well. The research done by Chong and Teng (1986) found a strong relationship between CN and a combination of the Using measured
and or
parameters (Equation 2.11) as well as and
alone (Equation 2.10).
data from each site to develop CNs with the equations from
Chong and Teng (1986) (Method 1 and Method 2) (Figure 2.3) showed there were only significant differences between the two methods at the Campus – East Urban Altered site and the two heavily vegetated prairie sites (α = 0.05). Additionally, the CV tended to be smaller for only estimated (Method 1 estimated) CNs compared to the CNs with study averages of 29% versus 36%.
33
and
estimated (Method 2)
-
Figure 2.3. United States Department of Agriculture - Natural Resource Conservation Service CN values calculated for 15 sites in Kansas from observed saturated hydraulic conductivity only (Method 1 conductivity and sorptivity (Method 2 -
and
) and observed saturated hydraulic
). The top and bottom of the box indicates the 75th and 25th percentiles,
respectively, and the solid line contained in the box is the median value. The dotted line indicates the arithmetic mean. The top and bottom of the whiskers indicate the 90th and 10th percentiles, respectively. For the Johnson County Transit site, the Method 3 CN of the primary land use (residential 2000 m2 lots) was used for comparison as the majority of infiltration tests were done with this land use.
34
Comparing measured and estimated strong trend in the
and CN values to soil survey data showed a
data and a similar but less clear trend with the two methods of CN
calculation (Table 2.3). In general, as site disturbance was reduced, the
and CN values from
Method 1 and 2 converged with data from Method 3. Method 1 and 2 had exceptions to this with the prairie and rural unaltered sites due to the low Method 3 CN estimate for the Rural Shawnee Big Blue site, but nevertheless, the two highest percentages of difference for these methods were at the engineered and urban altered sites. In general terms, this indicates using Method 3 CN values will likely be suitable for unaltered sites, which is consistent with the development of the CN method on small agricultural watersheds (Mishra and Singh, 2003).
Table 2.3. Difference between measured saturated hydraulic conductivity (
) and soil survey estimated
. Difference
between Curve Number (CN) estimates from soil survey soils data and best matching land cover (Method 3- Soil Survey) and the mean and median values for Method 1, which related
to CN, and the mean and median of Method 2, which related
and sorptivity ( ) to CN. Each mean and median, and soil survey value is weighted based on the number of observations at a site relative to the total number of samples in the site land use classification.
Soil Survey Site Land Use Classification
Method 1 CN *
Method 2 CN and So*
Method 3 CN Soil Survey Observation Weighted
Mean (Median)
Observation Weighted
Mean (Median)
Mean (Median)
% difference
mm/s
% difference
% difference
782 (169)
1.08
17.8 (21.2)
22.3 (25.1)
84.0
Urban Altered
158 (113)
2.48
14.1 (12.7)
19.2 (14.1)
75.7
Rural Altered
123 (97.9)
1.08
2.02 (6.83)
3.24 (13.1)
74.0
Prairie
90.8 (12.4)
1.64
3.37 (3.51)
12.6 (3.65)
75.0
Rural Unaltered
6.33 (2.05)
1.08
12.9 (12.1)
14.0 (17.4)
79.5
Engineered
* Emperical relationships relating CN to
developed by Chong & Teng (1986) (Equations 2.10 and 2.11 page 27)
The median values of the measured
data tended to be closer to the soil survey
values, potentially indicating the median metric may be a more suitable measure of the
at a
given site than the mean. This was not necessarily the case for the CN values, where there was no consistent trend between the magnitude of difference for either the median or the mean when compared to the Method 3 CN values. Additionally, Method 1 CN results were closer to Method
35
3 CN values than Method 2 values whether using mean or median values. Also, considering
is
neither a practical nor a common parameter, for people quantifying runoff and designing stormwater BMPs, it follows that Method 2 is far surpassed by Method 1 in terms of practicality and accuracy. Comparing Method 1 CN values at the Ft. Riley site to the mean measured CN based on runoff (Method 4) showed the mean CN from Method 1 would under predict CN at the site by 2.7% and over predict CN by 4.0% when using the median (Table 2.4). The addition of
in the
calculation (Method 2) decreased the estimated CNs, showing Method 2 is less favorable when estimating an appropriate CN in a prairie land use. Comparing Method 3 CN values to Method 4 at Ft. Riley yielded identical values, indicating the use of soil and land use data may give accurate runoff results in undisturbed or unaltered areas.
Table 2.4. Curve Number (CN) estimates based on saturated hydraulic conductivity ( measurements (Method 1 -
, Method 2 -
) and sorptivity ( )
) from Chong and Teng (1986) (Equations 2.10 and 2.11 page 27), estimates
based on soil survey soils data (Method 3 - Soil Survey), and estimates based on observed runoff data (Method 4 - Runoff). The Johnson County Transit (JCT) site had three land cover phases with the majority of measurements taken during the residential phase.
Site/land use
Method 1 CN Mean (Median)
Method 2 CN and So Mean (Median) 65 (72) 74 (88)†
Ft. Riley – Prairie 73 (78) JCT – 2000 m2 Residential 72 (82)† Lots JCT – Cultivated Land ˠ ˠ Without Conservation Treatment JCT – Open Spaces - Fair 63 (66)† 50 (45)† Condition The † indicates that the CN was adjusted for impervious area. The ˠ indicates that no infiltration tests were done during this land use phase. The * indicates bare or nearly bare ground April 2008 and May 2008. The ‡ indicates early establishment of vegetation June and July 2008.
Method 3 CN - Soil Survey
Method 4 CN - Runoff Mean (Median)
75 74
75 (74) 82 (88)
85†
100 (100)*
73†
92 (94)‡
At the JCT location, Method 1, Method 2, and Method 3 CN values were lower than Method 4 for the three land uses, indicating potential runoff would be underestimated. With the
36
primary land use at JCT (Residential Lots), the mean values from both Method 1 and Method 2 were lower than observed; however, the median value from Method 1 matched the mean value of Method 4 and the median value of Method 2 matched the median value of Method 4. Additionally, Method 2 CN values were lower than Method 1 during the early stages of vegetation establishment (JCT – Open Spaces - Fair Condition) and both were lower than Method 3 values, although all methods produced values substantially lower than Method 4. The lack of an enhanced CN estimate accuracy trend when using Method 2 at all four location/land use combinations in Table 2.4 contradicts the plot scale findings of Chong and Teng (1986) (Equation 2.10 & Equation 2.11), where the presence of So increased prediction accuracy. Comparing Method 1with Method 4 showed using median CN values from Method 1 would slightly over predict the mean runoff based CN at the Ft. Riley site while using the mean of Method 1 would slightly under predict the CN (Figure 2.4). Using Method 3 CN to predict mean Method 4 CN at the Ft. Riley (Prairie) site was more accurate than estimating CN with Method 1. However, at the Johnson County Transit site, median Method 1 CN predicted the mean Method 4 CN more closely than estimating CN with Method 3. Using Method 3 at the JCT site would cause a substantial error in estimated runoff.
37
Figure 2.4. Comparison of measured runoff calculated Curve Numbers (CN) from two sites in Kansas and the Chong and Teng (1986) CN saturated hydraulic conductivity ( CN values for runoff (horizontal boxes) and
) relationship. The box and whisker plots show the range of estimated
estimated (vertical boxes). The top (and right) and bottom (and left) of the
box indicates the 75th and 25th percentiles, respectively, and the solid line contained in the box is the median value. The dotted line indicates the arithmetic mean. The top and bottom of the whiskers indicate the 90th and 10th percentiles, respectively. A 1:1 relationship shown with solid black line. Data displayed for the Johnson County Transit site is for the “residential” phase of the study.
The median CN value from Method 1 at the JCT site (residential phase) was 0.6% higher than mean observed Method 4 CN. This close estimation was not consistent, however, in the prairie redevelopment phase of this site, where the median Method 1 CN value was 28.7% lower than Method 4 values. As noted earlier, Method 2 and Method 3 CN values were also lower than
38
observed. The conclusion is drawn, then, that estimating CN (and ensuing runoff) during times of land use transition is likely more difficult than utilizing an established land use. A positive correlation between Method 3 – Soil Survey CN and Method 1 –
CN
existed in the less disturbed sites (Rural Altered, Prairie, and Rural Unaltered) (Figure 2.5). Although this relationship was poor (R2 of 0.0934 with a RMSE of 7.52), the fact the trend is positive is a possible reflection on the suitability of soil survey CN estimates for less disturbed land uses.
Figure 2.5. Correlation between Method 3 - Soil Survey estimates of CN at sites that were heavily disturbed or altered (Engineered and Urban Altered sites) and at sites where less disturbance occurred (Rural Altered, Prairie, and Rural Unaltered) and Method 1 -
(Chong and Teng, 1986) estimated CN. A positive correlation existed between Method 3 and
Method 1 when considering the less disturbed sites (R2 of 0.0934 and a RMSE of 7.52) while a negative correlation existed between Method 3 and Method 1 when considering the highly disturbed sites (R2 of 0.00103 and a RMSE of 22.1).
There was a very poor (R2 of 0.00103 with a RMSE of 22.1) negative correlation between Method 3 and Method 1 in the highly disturbed sites (Engineered and Urban Altered),
39
which highlights the unpredictability of urban systems. Additionally, using Method 3 in urban areas could lead to substantial errors in runoff estimates. Since there was no predictable trend in Method 1 and Method 3 differences when considering the highly disturbed sites, stormwater BMPs designed based on Method 3 may be either over or under sized.
CONCLUSION These results show infiltration tests provide a more accurate CN in areas where soils have been altered from natural conditions through compaction and cut or fill operations. Additionally, median CN values rather than mean values developed from Method 1 and Method 2 at the Johnson County Transit site more closely predicted mean CN developed from Method 4 (runoff). The design implications of these findings could be undersized stormwater facilities in areas with altered soils when using the common Method 3. This trend would likely be more prominent in highly disturbed urban areas (Pitt et al., 1999; Woltemade, 2010). Undersizing stormwater facilities can lead to catastrophic failure of the water control structures as well as poor pollutant removal and poor hydraulic performance. Additionally, installation of a BMP in the wrong location can also cause more damage than no BMP (CWP, 1998). The Curve Number developed from Soil Survey values (Method 3) was the same as that developed from runoff data when considering the unaltered Ft. Riley site, which is a good indication no hydraulic conductivity tests would be needed in a location like this. Additionally, since runoff from unaltered or undisturbed areas is generally not as large of a concern for stormwater engineers, errors while estimating CN values are likely not of major importance.
REFERENCES ASTM. 2006. Standard Test Method for Infiltration Rate of Soils in Field using Double-Ring Infiltrometer. ASTM International, West Conshohocken, PA.
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Bach, L.B., P.J. Wierenga, and T.J. Ward. 1986. Estimation of the Philip Infiltration Parameters from Rainfall Simulation Data. Soil Science Society of America Journal 50:1319-1323. Baver, L.D. 1940. Soil Physics John Wiley & Sons, Inc., New York, NY. Chen, C.I. 1981. An evaluation of the mathematics and physical significance of the soil conservation service curve number procedure for estimating runoff volume. p. 387-418 Proc. The International Symposium on Rainfall-Runoff Modeling, Littleton, CO1981. Water Resources. Chong, S.K. 1983. Calculation of Sorptivity from Constant-Rate Rainfall Infiltration Measurement. Soil Science Society of America Journal 47:627-630. Chong, S.K., and T.M. Teng. 1986. Relationship between the runoff curve number and hydrologic soil properties. Journal of Hydrology 84:1-7. Chow, V.T., D.R. Maidment, and L.W. Mays. 1988. Applied Hydrology McGraw-Hill, New York, NY. Christianson, R.D., K.M. Kingery-Page, S.L. Hutchinson, and T.D. Keane. 2008. Challenges in Implementation. Proc. ASABE Annual International Meeting, Providence, RI2008. ASABE. Clothier, B.E. 2001. Infiltration, p. 239-280, In K. A. Smith and C. E. Mullins, (eds.) Soil and Environmental Analysis: Physical Methods. 2nd ed. Marcel Dekker, Inc., New York, NY. Collis-George, N. 1977. Infiltration equations for simple soil systems. Water Resources Research 13:395-403. Culbertson, T. 2008. Ecological Implications for Sustainable Stormwater Systems in the Tallgrass Prairie Region. MS, Kansas State University, Manhattan, KS. CWP. 1998. The Tools of Watershed Protection, p. 26, In D. Caraco, et al., (eds.) Rapid Watershed Planning Handbook: A Comprehensive Guide for Managing Urbanizing Watersheds. ed. The Center For Watershed Protection, Ellicott City, MD.
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Dangler, E.W., S.A. El-Swaify, L.R. Ahuja, and A.P. Barnett. 1976. Erodibility of selected Hawaii soils by rainfall simulation. USDA ARS; University of Hawaii Agricultural Experiment Station, Washington, DC. Fox, H.D., and S.N. Miller. 2003. Walnut Gulch Experimental Watershed (WGEW) Database Support. p. 562-566 In K. G. Renard, et al. (eds.) Proc. First Interagency Conference on Research in the Watersheds, Benson, AZ2003. U.S. Department of Agriculture, Agricultural Research Service. Friedman, D., C. Montana, P. Welle, C. Smith, and D. Lamm. 2001. Impact of Soil Disturbance During Construction on Bulk Density and Infiltration in Ocean County, New Jersey. Ocean County Soil Conservation District; Schnabel Engineering Associates, Inc.; USDA Natural Resources Conservation Service, Forked River, NJ. Gregory, J., M. Dukes, P. Jones, and G. Miller. 2006. Effect of urban soil compaction on infiltration rate. Journal of Soil and Water Conservation 61:117-124. Gumaa, G.S. 1978. Spatial variability of in-situ available water. PhD, University of Arizona, Tucson, AZ. Haan, C.T., B.J. Barfield, and J.C. Hayes. 1994. Design Hydrology and Sedimentology for Small Catchments Academic Press. Haverkamp, R., J.Y. Parlange, J.L. Starr, G. Schmitz, and C. Fuentes. 1990. Infiltration Under Ponded Conditions: 3. A Predictive Equation Based on Physical Parameters. Soil Science 149:292-300. Hawkins, R.H. 1973. Improved Prediction of Storm Runoff in Mountain Watersheds. Journal of the Irrigation and Drainage Division 99:519-523. Hawkins, R.H. 1993. Asymptotic Determination of Runoff Curve Numbers from Data. Journal of Irrigation and Drainage Engineering 119:334-345.
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Hawkins, R.H., T.J. Ward, D.E. Woodward, and J.A. Van Mullem. 2005. Progress Report: ASCE Task Committee on Curve Number Hydrology. Proc. The 2005 Watershed Management Conference, Williamsburg, VA2005. ASCE. Hawkins, R.H., T.J. Ward, D.E. Woodward, and J.A. Van Mullem. 2009. Curve Number Hydrology: State of the Practice ASCE, Reston, VA. Hunt, W., and N. White. 2001. Designing Rain Gardens (Bio-Retention Areas), In N. C. S. University, (ed.), Vol. AG-588-3. North Carolina Cooperative Extension Service, Raleigh, NC. Hutchinson, S.L., T. Keane, R.D. Christianson, L. Skabeland, T.L. Moore, A.M. Greene, and K. Kingery-Page. 2011. Management practices for the amelioration of urban stormwater. Procedia Environmental Sciences 9:83-89. Kim, I.J. 2006. Identifying the Roles of Overland Flow Characteristics and Vegetated Buffer Systems for Nonpoint Source Pollution Control. PhD, Kansas State University, Manhattan, Kansas. Legg, A.D., R.T. Bannerman, and J. Panuska. 1996. Variation in the Relation of Rainfall to Runoff from Residential Lawns in Madison, Wisconsin, July and August 1995. USGS, Madison, WI. Lewis, M.R., and W.L. Powers. 1938. A Study of Factors Affecting Infiltration. p. 334-339 Proc. Soil Science Society Proceedings1938. Linsley, R.K.J., M.A. Kohler, and J.L.H. Paulhus. 1949. Applied Hydrology McGraw-Hill Book Company, Inc., New York, NY. Mishra, S.K., and V.P. Singh. 2003. Soil Conservation Service Curve Number (SCS-CN) Methodology Kluwer Academic Publishers, Dordrecht, The Netherlands. Mockus, V. 1972. Estimation of Direct Runoff from Storm Rainfall National Engineering Handbook. ed. Soil Conservation Service, Washington, DC.
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Morel-Seytoux, H.J., and J.P. Verdin. 1983. Correspondence Between the SCS CN and Infiltration Parameters (K, Sf), p. 308-319, In J. Borrelli, et al., (eds.) Advances in Irrigation and Drainage. ed. American Society of Civil Engineers, New York, NY. National Atlas. 2011. Climate Maps of the United States. Available at http://nationalatlas.gov/printable/climatemap.html#list (accessed 11/23/2011). Nielsen, D.R., J.W. Biggar, and K.T. Erh. 1973. Spatial Variability of Field-Measured Soil-Water Properties. Hilgardia 42:215-259. NOAA. 2011. National Weather Service JetStream - Online School for Weather. Available at http://www.srh.noaa.gov/jetstream/global/climate.htm (accessed 11/23/2011). NOAA. PGCMD. 2009. Bioretention Manual. Prince George's County, MD, Prince George's County, MD. Philip, J.R. 1957. The Theory of Infiltration: 4. Sorptivity and Algebraic Infiltration Equations. Soil Science 84:257-264. Philip, J.R. 1958. The Theory of Infiltration: 6 Effect of Water Depth Over Soil. Soil Science 85:278-286. Pitt, R., J. Lantrip, R. Harrison, C.L. Henry, D. Xue, and T.P. O'Connor. 1999. Infiltration Through Disturbed Urban Soils and Compost-Amended Soil Effects on Runoff Quality and Quantity. USEPA, Washington, DC. Ponce, V., and R. Hawkins. 1996. Runoff Curve Number: Has it Reached Maturity? Journal of Hydrologic Engineering 1:11-19. Rogowski, A.S. 1972. Watershed Physics - Soil Variability Criteria. Water Resources Research 8:1015-&. Satchithanantham, S. 2008. Evaluation of Vegetated Filter Strips for Attenuation of Pollutants Resulting from Military Activities. MS, Kansas State University, Manhattan, Kansas.
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Scott, H.D. 2000. Soil Water Flow Processes in the Field, p. 244-281, In H. D. Scott, (ed.) Soil Physics: Agricultural and Environmental Applications. 1st ed. Iowa State University Press, Ames, IA. Seelig, B. 1993. Soil Survey: The Foundation for Productive Natural Resource Management, In N. D. S. University, (ed.) Extension Bulletin, Vol. 60. North Dakota State University, Department of Agronomy, Fargo, ND. Sharma, M.L., G.A. Gander, and C.G. Hunt. 1980. Spatial Variability of Infiltration in a Watershed. Journal of Hydrology 45:101-122. Smettem, K.R.J. 2002. Soils Role in the Hydrologic Cycle, p. 671-673, In R. Lal, (ed.) Encyclopedia of Soil Science. ed. Marcel Dekker, Inc., New York, NY. Smiles, D.E., and J.H. Knight. 1976. Use of Philip Infiltration Equation. Australian Journal of Soil Research 14:103-108. Snedecor, G.W. 1946. Statistical Methods The Collegiate Press, Inc., Ames, IA. St. Clair, A.K. 2007. Suitability of Tallgrass Prairie Filter Strips for Control of Non-Point Source Pollution Originating from Military Activities. MS, Kansas State University, Manhattan, Kansas. Talsma, T. 1969. In situ measurement of sorptivity. Australian Journal of Soil Research 7:269276. USDA. 2009. National Soil Information System (NASIS) | NRCS Soils. Available at http://soils.usda.gov/technical/nasis/ (accessed 7/24/2011). USDA - NRCS. USDA-NRCS. 1986. Urban Hydrology for Small Watersheds. Natural Resources Conservation Service, Washington, DC. USDA-NRCS. 2004. Estimation of Direct Runoff from Storm Rainfall. National Engineering Handbook, Part 630 Hydrology. USDA-NRCS, Washington, DC. USDA-NRCS. 2007. Hydrologic Soil Groups. National Engineering Handbook, Part 630 Hydrology. USDA-NRCS, Washington, DC. 45
USDA-NRCS. 2011. Web Soil Survey. Release Release 2.3. United States Department of Agriculture: Natural Resources Conservation Service. Warrick, A.W., and D.R. Nielsen. 1980. Spatial Variability of Soil Physical Properties in the Field, In D. Hillel, (ed.) Applications of Soil Physics. ed. Academic Press, New York, NY. Woltemade, C.J. 2010. Impact of Residential Soil Disturbance on Infiltration Rate and Stormwater Runoff1. Journal of the American Water Resources Association 46:700-711. Youngs, E.G. 1968. An Estimation of Sorptivity for Infiltration Studies From Moisture Moment Considerations. Soil Science 106:157-163. Zhang, R. 1997. Determination of soil sorptivity and hydraulic conductivity from the disk infiltrometer. Soil Science Society of America Journal 61:1024-1030.
46
CHAPTER III
DEVELOPMENT OF A BIORETENTION CELL MODEL AND EVALUATION OF INPUT SPECIFICITY ON MODEL ACCURACY
The following chapter has been accepted for publication in Transactions of the ASABE and appears in this thesis with the journal’s permission.
ABSTRACT With the implementation of Phase II of the National Pollutant Discharge Elimination System (NPDES), municipalities have new requirements to reduce stormwater quantity and enhance water quality. Bioretention cells (BRCs) are a pollution mitigation option that can address the new regulations. In order to implement BRCs in the landscape, models are needed so stormwater engineers and managers can estimate the impact of the mitigation technique. While several BRC models are available, users must supply input parameters, which are many times poorly understood. The objective of this work was to determine the level of input specificity in hydraulic parameters needed to accurately estimate water movement through a BRC. A water movement model was developed, which incorporates infiltration, drainage, and overflow for a single storm event. Then pilot-scale BRCs were constructed and operated to obtain data for model testing. The model was run with four sets of input parameters with increasing specificity; soil 47
type, fraction sand/silt/clay, an adjustment for bulk density, and a macropore routine to serve as a fitting parameter. While the model with the highest input specificity proved to match experimental values closest (drainage volume between 0.7% and 8.8% from observed and maximum drainage flowrate between 1.4% and 18% from observed), it is unlikely stormwater managers would have access or time to obtain this information. However, a simulation with the fraction sand/silt/clay and an adjustment for bulk density provided acceptable results (drainage volume between 0.7% and 18% from observed and maximum drainage flowrate between 30% and 39% from observed).
INTRODUCTION The U.S. Environmental Protection Agency (USEPA) is currently enforcing urban diffuse pollution through the National Pollution Discharge Elimination System (NPDES) (USEPA, 2002a; USEPA, 2011). The NPDES is an environmental program that aims to reduce water pollution by requiring municipalities to regulate runoff water quality and quantity. Urban population growth and suburban sprawl can decrease stream quality due to increases in impervious area (Brown, 2000), with as little as 14% imperviousness having a detrimental impact (Caraco, 2000; Neller, 1998). In many such urban areas, increased runoff quantity due to the addition of impervious or less pervious surfaces can cause flash flooding (Hollis, 1975) and can decrease water quality via increased erosion (Shaver et al., 2007), which may contribute up to two-thirds the total sediment load (Trimble, 1997). To meet NPDES requirements, there are many stormwater control measures or stormwater best management practices (BMPs) designed to mitigate the impact of urban runoff (Jeer, 1997). Popular options include wetlands, vegetative filter strips, dry ponds, and bioretention cells. Specifically, bioretention cells can aid in improving water quality, reducing the peak runoff flowrate, and reducing the volume of runoff generated by a storm.
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Due to the significant capital investment requirements of installing a bioretention cell, prior knowledge of the system performance during common storm events is important. Currently, there are several computer models and sizing routines available that have the capability to model bioretention systems (Dussaillant et al., 2004; Dussaillant, 2002; PGCMD, 2004; Voorhees and Pitt, 2011). However, these models limit soil layers and hydrograph timing. For example, the most comprehensive model available for evaluation, RECARGA, is limited to a maximum of three soil layers with the drain located after the first layer (Atchison and Severson, 2004), which may not be reflective of many bioretention cell designs. Another comprehensive BRC model, SUSTAIN (USEPA, 2009), uses the Green-Ampt approach to modeling infiltration, but only allows for one homogeneous layer of infiltration media before the underdrain. The BRC model described in this paper was developed to allow modeling of more complex BRC designs. However, there is uncertainty about the level of specificity of the input parameters. In other words, to accurately simulate water movement in a BRC, is the use of literature-derived soil parameters sufficient, or rather, do more specific laboratory and/or fielddetermined soil parameters need to be used? When designing stormwater control facilities, environmental engineers prefer to use data that are easily obtained for the area of interest. The Soil Survey Geographic (SSURGO) database (USDA-NRCS, 2011) is widely used in geographic information systems (Fox and Miller, 2003; Niu et al., 2009), making this database attractive to state and local planners. This dataset gives the saturated hydraulic conductivity, porosity, and other pertinent information. However, these data may not necessarily represent the site (Seelig, 1993; Hempel et al., 2008) due to factors altering soil structure, such as land use changes, cut-and-fill operations, and construction compaction (Gregory et al., 2006). The USDA has recognized this issue and has created the National Soil Information System (NASIS) (USDA-NRCS, 2009) where registered users post screened data. Unless data have been gathered in the NASIS database for the location in question, it may be 49
necessary to acquire more site specific information. Infiltrometers can be used to collect the saturated hydraulic conductivity, and when used in conjunction with soil bulk density and soil type, site specific designs can be made. The objectives of this work were to (1) develop a 1-D water movement model for a layered stormwater BMP and (2) use pilot-scale infiltration experiments to determine the accuracy and precision of model input parameters derived from different sources. While the conclusions are based on the performance of a single model, it is believed they are applicable to other models with similar theoretical foundations.
METHODS MODEL DESCRIPTION
The BRC model was developed as part of a multi-BMP model – Integrated Design, Evaluation, and Assessment of Loadings (IDEAL) (Barfield et al., 2003) – for the evaluation of stormwater practices working in series or parallel. The BRC portion included water movement processes of infiltration, drainage and overflow (Figure 3.1).
Figure 3.1. Hydraulic processes considered in the bioretention cell component model initially created for the Integrated Design, Evaluation, and Assessment of Loadings (IDEAL) model.
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Infiltration is the key component of any BRC model. Extending the method of Haan et al. (1994) to allow for ponded water ( ) in the Green-Ampt model, Equation 3.1 allows for the calculation of cumulative infiltration with time.
( )
where
(
( ))
(
( ) (
( ))
is the total infiltration from the start of the event,
conductivity, is time
)
(3.1)
is the saturated hydraulic
is the capillary suction at the wetting front,
is the increase in water
content behind the wetting front and ( )is the depth of water ponded on the surface which was determined using the surface area of the BRC and the volume difference between inflow and infiltration. This form of the Green-Ampt infiltration model requires an iterative solution as ( ) appears on both sides of the equation. An iterative approach was also used to dampen over- and under-estimation of ( ) and ( ). Three iterations were used with a time step of 0.005 hours as preliminary results showed this eliminated nearly all discrepancies in the mass balance (
