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ENGINEERING URBAN TRANSPORATION INFRASTRUCTURE TO MITIGATE THERMAL POLLUTION IN STORMWATER RAINFALL-RUNOFF USING SOURCE

CONTROL METHODS

By

RUBEN ALEXANDER KERTESZ

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2011

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© 2011 Ruben Kertesz

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To everybody who has encouraged me and supported my desire to explore our relationship in the global environment and to God for giving me the chance to share it

with others.

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ACKNOWLEDGMENTS

I thank my family for supporting my move into engineering. I thank Dr. Lindner for

bringing me to the University of Florida and I thank Dr. Heaney for encouraging me to

build my understanding of water conservation and computational techniques. I thank Dr.

Sansalone for allowing me to take classes to become a licensed engineer and for

encouraging me to pursue thermal pollution. I thank Dr. Huber for his guidance and

flexibility. I thank Dr. Bloomquist for his instruction and his enlightening comments. I

thank John Mocko for giving me access to campus weather data and to Demetris

Athienitis for assistance in statistical analysis. I thank the Florida Education Fund for

providing financial support. I thank my lab mates, my friends, and my significant other

who have listened to me share my findings.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 7

LIST OF FIGURES .......................................................................................................... 9

LIST OF ABBREVIATIONS ........................................................................................... 12

ABSTRACT ................................................................................................................... 13

CHAPTER

1 GLOBAL INTRODUCTION ..................................................................................... 15

2 HYDROLOGIC TRANSPORT AND FIRST FLUSH OF THERMAL LOAD FROM ASPHALTIC PAVEMENT ....................................................................................... 17

Background ............................................................................................................. 17 Objectives ............................................................................................................... 19 Methodology ........................................................................................................... 19

Data Collection Methods .................................................................................. 20 Calculation Methods for Temporal Distribution of Heat Transfer to Runoff

During Event ................................................................................................. 21 Method Components of Heat Balance Models ................................................. 22

Radiation .................................................................................................... 22 Heat loss by evaporation............................................................................ 24 Sensible heat loss ...................................................................................... 25 Heat loss by convection ............................................................................. 25

Substitution of Runoff Temperature for Pavement Surface Temperature ......... 26 Results and Discussion........................................................................................... 26

Heat Transfer to Runoff during an Event .......................................................... 26 Impact of hydrologic parameters on heat transfer ...................................... 27 Relationship between antecedent pavement temperature and heat

transfer ................................................................................................... 28 Impact of event date and start time on heat transfer .................................. 29

Heat Balance Model Comparison ..................................................................... 29 Discussion .............................................................................................................. 31 Summary ................................................................................................................ 33

3 CYCLIC TEMPERATURE PROFILES FOR ASPHALTIC PAVEMENT AS A FUNCTION OF TREE CANOPY SHADING AND VEHICULAR PARKING FREQUENCY ......................................................................................................... 49

Background ............................................................................................................. 49

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Objective ................................................................................................................. 51 Methodology ........................................................................................................... 51

Parking Stall Data Collection Methods ............................................................. 52 Simulated Driving Activity Data Collection ........................................................ 54 Tree Canopy Shade Data Collection Methods ................................................. 55

Results and Discussion........................................................................................... 57 Thermal Results of Parking Stall Shade Treatments ........................................ 57 Pavement Temperature Shift Under Simulated Parking Activity ....................... 58 Thermal Trends on Shaded Roadway .............................................................. 61

Summary ................................................................................................................ 64

4 MITIGATING URBAN HEAT: TEMPORAL TEMPERATURE PROFILES FOR PAVEMENT MATERIALS ....................................................................................... 81

Background ............................................................................................................. 81 Objective ................................................................................................................. 83 Methodology ........................................................................................................... 84

Data Collection Methods .................................................................................. 84 CFD Model Components of Heat Transfer with Solar Radiation ...................... 86 Simulation Methods for Temporal Distribution of Heat Transfer Under Solar

Radiation ....................................................................................................... 89 Results and discussion ........................................................................................... 90

Measured Heat Balance on Pavement ............................................................. 90 Heat Balance Simulation Model ....................................................................... 97

Summary ................................................................................................................ 98

5 COMPUTATIONAL MODELING OF OVERLAND FLOW AND HEAT TRANSFER IN ASPHALTIC PAVEMENTS .......................................................... 116

Background ........................................................................................................... 116 Objective ............................................................................................................... 120 Methodology ......................................................................................................... 120

Physical Experiments ..................................................................................... 121 Modeling Methodology ................................................................................... 123 Heat Transfer Calculation of Flow Over a Flat Plate ...................................... 128

Results and Discussion......................................................................................... 130 Summary .............................................................................................................. 135

6 GLOBAL CONCLUSION ....................................................................................... 146

LIST OF REFERENCES ............................................................................................. 149

BIOGRAPHICAL SKETCH .......................................................................................... 159

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LIST OF TABLES

Table page 2-1 Selected properties of asphalt pavement from various studies .......................... 35

2-2 Storm event data for measured rainfall events and Kolomogorov-Smirnov test for goodness of fit ........................................................................................ 36

2-3 Correlations between storm event parameters. .................................................. 37

2-4 Tabular pavement and subgrade temperature profiles at beginning and end of storm. ............................................................................................................. 38

2-5 Total NHT for various modeling methods compared to measured values. Negative values represent heat gain by pavement. ............................................ 38

3-1 Weather conditions during 18 September and 19 September calibration days. . 65

3-2 Weather data during parking experiment performed on 4 October, 2010. .......... 65

3-3 Parametric statistics for hysteretic loop equations for 19 October, 2010 experiment. ......................................................................................................... 66

3-4 Parametric statistics for hysteretic loops equations for 28 October, 2010 experiment. ......................................................................................................... 66

3-5 Hourly asphalt pavement temperatures across east-west transect. ................... 67

3-6 Daily solar radiation, air temperature, wind, and shadow patterns. .................... 68

3-7 Shadow patterns over transect, measured from west curb ................................. 69

3-8 Average annual benefits of four tree sizes over 40 year period. ......................... 69

4-1 Thermal and physical properties of pavement .................................................. 100

4-2 Model parameters for computational simulation ............................................... 100

4-3 Properties of air and expanded polystyrene (EPS) ........................................... 100

4-4 Median values of pavement heat cycle for all measured days. ........................ 101

4-5 Integration of pavement heat cycle heat for 8 September to 10 September. .... 101

5-1 Thermal and physical properties of pavement .................................................. 136

5-2 Material parameters used in computational fluid dynamics simulation ............. 137

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5-3 Model parameters for computational simulation ............................................... 138

5-4 Analysis of error between modeled and measured results. .............................. 139

5-5 Analysis of error between modeled and measured results with implicit body force and specified operating density. .............................................................. 139

5-6 Analysis of error between modeled and measured results with 50% evaporation/condensation threshold. ................................................................ 140

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LIST OF FIGURES

Figure page 2-1 Historical monthly distribution of weather data for Gainesville, FL and

Portland, OR ....................................................................................................... 39

2-2 Lake Alice watershed including subject catchment (~450 m2). ........................... 39

2-3 Plan and cross-sectional view of thermocouples (TC) for catchment pavement system in Lake Alice watershed. ........................................................ 40

2-4 Conceptual pavement heat balance model with nominal thermocouple installation depths. .............................................................................................. 40

2-5 Low flow rate storm event data recorded on June 23, 2008. .............................. 41

2-6 Moderate flow rate storm event data recorded on June 30, 2008 ...................... 42

2-7 Storm event data recorded on August 21, 2008 (Tropical Storm Fay). ............... 43

2-8 Distributions of cumulative heat and cumulative flow for 12 storms that are similar according to K-S tests ............................................................................. 44

2-9 Modeled storm event data showing only best fit models for A) 14 July 2008 and B) 12 August 2008. ...................................................................................... 45

2-10 Modeled storm event data showing only best fit models for A) 21 August 2008 and B) September 10 2008 ........................................................................ 46

2-11 Residual values for four models.. ....................................................................... 47

2-12 Median temperature at two depths in a 38mm asphalt pavement with a forced wind velocity of 2.2 m/s over the pavement surface. ............................... 48

3-1 Lake Alice watershed including parking lot catchment, transect, and parking spaces investigated herein. ................................................................................ 70

3-2 Vehicle body and asphalt surface thermocouple installation diagram.. .............. 71

3-3 Vehicular surface temperatures measured in direct sunlight for the A) roof, B) hood, and C) trunk during calibration period. ...................................................... 72

3-4 Pavement surface temperatures beneath engine (front) and gas tank (rear) of vehicles A and B exposed to direct sunlight during calibration period. ............... 73

3-5 Comparison of average surface and pavement temperatures between shaded and unshaded vehicles between the hours of 10:00 and 17:00. ............ 74

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3-6 Pavement temperature A) before, B) during, and C) after driving test vehicle to observe effect of warm engine on 4 October, 2010. ....................................... 75

3-7 Pavement surface temperature under frequent parking A) on 19 October and B) on 28 October ................................................................................................ 76

3-8 Pavement surface temperature hysteretic loops on 19 October 2010 beneath front and rear of vehicle. Three cycles are shown. ............................................. 77

3-9 Pavement surface temperature hysteretic loops on 28 October 2010 beneath front and rear of vehicle. Three cycles are shown. ............................................. 78

3-10 Graphic analysis of shadow patterns over pavement surface for daytime hours. ................................................................................................................. 79

3-11 Plot of heat transfer to runoff compared to pavement temperature before storm. ................................................................................................................. 80

4-1 Comparison of rainfall pattern frequency by hour from 10 years of hourly rainfall data collected in two climates in the United States. .............................. 102

4-2 Schematic of simulation geometry.. .................................................................. 103

4-3 Comparison of temperatures at surface and interior of pavements, 15 September, 2010. ............................................................................................. 104

4-4 Relative distribution of rainfall event occurrence and total rainfall depth by day-hour during the rainy season in Gainesville, FL. ........................................ 105

4-5 Mean hourly temperature and heat absorption with standard deviation. KJ are per unit area 1m2. ....................................................................................... 106

4-6 Relative impact index (RII) for pavement heat storage reduction in Gainesville, FL (negative is better). .................................................................. 107

4-7 Comparison of cumulative heat storage in pavement and atmospheric conditions between 8 September and 11 September, 2010.. ........................... 108

4-8 Comparison of pavement temperature before, during, and after two rain events of differing intensity and time of day. ..................................................... 109

4-9 Comparison of thermal heating pattern on two dry days of differing radiation on A) 17 September and B) 10 September ...................................................... 110

4-10 Concrete temperature and asphalt temperature at A) east side of road and B) west side of road; C) difference between concrete and asphalt at both locations ........................................................................................................... 111

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4-11 Modeled pavement temperature for control asphalt and white asphalt pavements on 18 August, 2010. ....................................................................... 112

4-12 Comparison of modeled pavement temperature results under for current, low, and high thermal conductivity (k) values for reflective asphalt simulation. ........ 113

4-13 Measured vs. modeled asphalt temperatures for two days in August, 2010. .... 114

4-14 A comparison of measured and modeled asphalt and concrete temperatures on 6 September, 2010. ..................................................................................... 115

5-1 Installation of thermocouples in pavement specimen ....................................... 141

5-2 CFD mesh dimensions and statistics. ............................................................... 142

5-3 Measured and modeled asphalt specimen temperature and effluent temperature. ..................................................................................................... 143

5-4 Measured and modeled concrete specimen temperature and effluent temperature.. .................................................................................................... 144

5-5 Effluent temperature modeled using flat plate method. .................................... 145

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LIST OF ABBREVIATIONS

BMP best management practice

CDF cumulative distribution function

CFD computational fluid dynamics

EPS expanded polystyrene

EST eastern standard time

FDA functional data analysis

FEA finite element analysis

HRIC high resolution interface capturing

HSPF hydrologic simulation program in fortran

LID Low Impact Development

NHT Net Heat Transfer

PIP Peak Insolation Period

PISO pressure-implicit with splitting operators

PRESTO pressure staggering option

QUICK quadratic upwind interpolation

RHT relative heat transfer

RMSE root mean squared error

RPD relative percent difference

RPE relative percent error

TC thermocouple

TMDL total maximum daily load

TRMPAVE thermal runoff model for pavement

TURM thermal urban runoff model

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

ENGINEERING URBAN TRANSPORATION INFRASTRUCTURE TO MITIGATE

THERMAL POLLUTION IN STORMWATER RAINFALL-RUNOFF USING SOURCE CONTROL METHODS

By

Ruben A. Kertesz

May 2011

Chair: Sansalone Major: Environmental Engineering Sciences

Research in the field of thermal pollution in urban areas has traditionally been

relegated to studies on the urban heat island effect or global climate change. Little

research has been performed to test for the effect of pavement temperature on

stormwater runoff. The research presented herein focuses on the measurement and

simulation of heat transfer to pavement by radiation and of heat transfer from the

pavement to rainfall-runoff. Four studies are performed to provide an understanding of

the mechanisms to limit thermal pollution.

The first study involves the measurement and simulation of heat transfer to

rainfall-runoff from an in-situ parking lot surface. Results from applying a series of

published heat balance models indicate that evaporation and long wave radiation are

important runoff event-based heat transfer mechanisms. The second study is designed

to determine the effect of shading and vehicular activity on pavement surface

temperature in an asphaltic parking lot. Results show that pavement temperature does

not differ significantly beneath a shaded and an unshaded vehicle, that there is a

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demonstrable effect of vehicle operation on pavement temperature, and that it is most

critical to shade pavement during the daily peak insolation period.

The third study provides a thermal comparison between the daytime temperatures

of three pavement specimens of differing material selection and surface treatments. A

computational analysis is compared to measured data. CFD model results are not

statistically significantly different from measured data for each pavement material.

Results indicate that adding a reflective coating to asphalt or utilizing concrete in lieu of

asphalt results in a 20% reduction in pavement heat load through the day. Concrete

pavement stores up to 55% less heat than asphalt between 12:00 and 19:00.

The fourth study investigates the applicability of a computational fluid dynamics

simulation to model heat transfer to overland flow from two pavement surfaces with the

intent of enhancing knowledge of the rainfall-runoff heat transfer relationships for

various pavement mix designs. Results from 300 seconds of simulation are compared

to measured results. Findings indicate that evaporation may only be critical within the

first seconds of runoff. The best CFD result is exhibited by the turbulent concrete

simulation with a 50% air/water threshold for evaporation/condensation to occur.

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CHAPTER 1 GLOBAL INTRODUCTION

The series of investigations herein are developed as an exploration into the

contribution of urban rainfall-runoff pollution from urban surfaces. Akbari et al. (2003)

reported that pavement covers 29% of Houston and 45% of Sacramento with 60% and

29% of these areas attributed to parking, respectively. Converting vegetated areas to

impervious areas reduces groundwater-fed streamflow, compounding thermal impacts

(Janke et al. 2009; Ferguson and Suckling 1990; Leith and Whitfield 2000; Horner et al.

1994).

Much research has already been performed on nutrient, metal, and hydrocarbon

pollution sources. Various treatment mechanisms have been proposed, some of which

are commonly used today. The most commonplace mechanisms involve temporarily or

permanently impounding water, allowing various physical and chemical processes to

remove pollution from receiving waters. However, in many parts of the United States,

stormwater is still discharged directly to receiving waters, whether they be lakes,

streams, the ocean, or, to a lesser extent, direct discharge to groundwater.

This dissertation focuses on a novel pollutant: heat. Heat pollution is novel for two

reasons. Most importantly, the effects of heat pollution on receiving water biota are only

recently being documented but construction practices have not yet advanced in

accordance with these findings. Secondly, heat is a transient property rather than a

persistent pollutant. In fact, many of the traditional methods of impoundment that

remove persistent pollutants can actually increase exposure to sunlight and therefore

heat content of the water. The transient nature of thermal pollution also makes it difficult

to determine the magnitude and timing of pollution discharge in urban areas without

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having intimate knowledge of the contributing source areas as well as surface and

subsurface flow routing connectivity.

Many low impact development methods have been proposed to minimize the

energy and land area required for traditional treatment, such as the use of bioretention

areas, subsurface exfiltration basins, both of which are often coupled with filter media,

using porous building materials, or simply disconnecting source areas from conduit

networks. By focusing on the source area, stormwater pollution, and particularly heat

pollution can be controlled systematically and successfully mitigated. It is even possible

to additionally treat more well understood pollutants while controlling for thermal

pollution. It is within the context of Low Impact Development (LID) that the following

chapters are written.

The testing sites are located in North-Central Florida. As a heat-conductive

interface, impervious asphalt pavement serves as a thermal reservoir for climates with

diverse conditions such as annual rainfall distributions. For example, Florida’s climate is

unique from Wisconsin (Roa-Espinosa et al. 2003), Ontario, CA (Van Buren et al. 2000;

James and Verspagen 1995), or Oregon (Haq and James 2002); locations of previous

thermal runoff studies. The predominance of Florida’s precipitation is coincident with the

warmest months; illustrating an inverted pattern to that of Oregon. Florida storms

typically occur during the mid-afternoon when pavement temperature is hottest but

rainwater is at dew point temperature. Hence, the studies benefit by a high signal to

noise ratio due to the very high pavement temperatures that are reached in the sunlight.

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CHAPTER 2 HYDROLOGIC TRANSPORT AND FIRST FLUSH OF THERMAL LOAD FROM

ASPHALTIC PAVEMENT

Background

Since the Industrial Revolution, thermal loads from urban environs have increased

(Sansalone 2002). Recently, impacts of imperviousness on thermal load and causal

mechanisms have been identified (Oke 1982; Mestayer and Anquetin 1994; Langford

1990). Akbari et al. (2003) reported that pavement covers 29% of Houston and 45% of

Sacramento with 60% and 29% of these areas attributed to parking, respectively.

Converting vegetated areas to impervious areas reduces groundwater-fed streamflow,

compounding thermal impacts (Janke et al. 2009; Ferguson and Suckling 1990; Leith

and Whitfield 2000; Horner et al. 1994). Asphalt can emit 130 W/m2 of radiation and

200 W/m2 sensible heat at mid-day, significantly above vegetated cover levels (Thanh

Ca et al. 1997). Asaeda et al. (1996) reported that asphalt temperatures can exceed

65°C. As a heat-conductive interface, impervious asphalt pavement serves as a

thermal reservoir even for diverse climates. For example, as shown in Figure 2-1, the

predominance of Florida’s precipitation is coincident with the warmest months; an

inverted pattern to that of Oregon.

Thermal load is a concern due to impacts on water chemistry and ecosystem

integrity of receiving waters such as increases in cold water stream temperatures

(Langford 1990; Galli 1990) and fish distress (Coutant 1987; Nakatani 1969; Paul and

Meyer, 2001). Urbanization and increased receiving water temperature are related

(Langford 1990). Galli (1990) reported that a 1% increase in imperviousness is related

to a 0.09°C increase in cold-water stream temperature with local extinction of trout and

stoneflies. Trout and salmon stressed by water above 21°C will change habitat

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(Coutant 1987). From 1979-1999, an increase of 0.83°C had a deleterious impact on

the Upper Rhone River based on indicator species (Daufresne et al. 2004). Armour

(1991) found increased Escherichia coli. levels due to thermal load. Thermal load can

reduce dissolved oxygen needed for fish and plant survival (Nakatani 1969; James and

Xie 1998; Paul and Meyer 2001) and can lead to increased metal toxicity (Davies 1986).

Few studies have measured pavement and runoff temperature during uncontrolled

transient event loadings. Studies focused on pavement temperature (Minhoto et al.

2005; Asaeda et al. 1996; Yavuzturk et al., 2005), thermal load of pavement runoff

(Krause et al. 2004; Haq and James 2002), and heat fluxes to and from pavement

surfaces (Anandakumar 1999; Than Ca et al. 1997; Herb et al. 2008). While steady

loadings have the advantage of a controlled load-response, the response to

uncontrolled transient loadings is also required. However, researchers reported that

study of actual rainfall-runoff events can be challenged by spatial, temporal, event-

frequency and number constraints (Roa-Espinosa et al. 2003, Janke et al. 2009, Van

Buren 2000).

In my study it is hypothesized that the transport of temperature and thermal load

by source area pavement runoff has analogs to the transport of constituent

concentration and mass, respectively. It has been shown that transport concepts such

as the first flush commonly utilized for design, regulation and control can be distilled

from many previous studies into either concentration or mass definitions (Sansalone

and Cristina 2004). Specifically, with respect to the transport of pollutant load, Sheng et

al. (2008) demonstrated by categorical analysis that the limiting transport classes for

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dissolved or particulate matter mass are mass limited (first-order mass or heat

transport) or flow limited (zero-order mass or heat transport).

Objectives

The primary objective of my study is to measure and model the intra-event

distribution of temperature and transport of thermal load in runoff from an asphaltic

pavement source area. The study hypothesizes that (1) thermal load delivery is

controlled by hydrology and can be primarily flow limited; (2) for a rainfall-runoff event,

the seasonal event date, event duration, antecedent weather parameters, and

pavement temperature are correlated with net heat transfer (NHT) to runoff; (3) for a

rainfall-runoff event, the subgrade temperature and intra-event weather conditions are

correlated with NHT. A second objective is to reproduce measured results utilizing heat

balance models. As part of this second objective, the study hypothesizes that: (1)

pavement heat conduction is a surrogate for overall heat transfer to runoff; and (2) that

runoff temperature is an appropriate substitute for pavement surface temperature. The

study combines measurement and modeling to illustrate the transport and potential of a

first-flush of thermal load for an asphalt-paved source area, illustrating the coupling of

hydrology and heat transfer.

Methodology

In my study, an outfall appurtenance located at 29.644098° N, 82.348404° W

drains an asphalt-paved catchment used for surface parking as shown in Figure 2-2.

The catchment is loaded by approximately 708 vehicles per weekday and 84 vehicles

per weekend day. The contributing drainage area is approximately 450 to 500 m2,

determined using light detection and ranging (LIDAR) data and onsite surveying, and is

dependent on rainfall intensity. The hot-mix asphalt pavement has a concrete curb and

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gutter. Trees surround the catchment, with two dense foliage trees on the west side of

the catchment and magnolia trees immediately east of the catchment.

Data Collection Methods

Thermal Thermal measurements are made using type-T Omega Inc. {5TC-PVC}

thermocouples (TCs). The catchment primary flow path is ground-truthed and a 5.6 m

transect of TCs is installed in the path of the sheet flow. Measurements are taken at

0.1m, 1.2m, 2.6m, 4.1m, and 5.3m from the east end (headwater) of the transect, and

concrete-gutter measurements at 0m and 5.6m from the east end of the transect for

“East Concrete” (EC) and “West Concrete” (WC), respectively. Figures 2-3 and 2-4

illustrate the spatial and depth locations of the TCs. Surface temperature is

approximated as a function of subsurface pavement temperature as shown in Equation

2-1.

(2-1)

In this equation, is the mean surface temperature (oC), is the temperature in the

pavement at 13mm (oC), is the temperature at location A5 and depth of 1mm,

and is the temperature at location A5 and depth of 13mm. Runoff temperature is

measured with two TCs placed at the invert of a 150mm PVC pipe conveying pavement

flows at the catchment outfall.

Tipping bucket rain data (increments of 0.254mm) are collected at 29.642891° N,

82.34864° W. At 29.639461° N, 82.345293° W a Texas Weather Instruments WRL-25

records solar radiation, ambient temperature, cloud cover, and wind. An AM25T

multiplexer measures TC data and a Campbell Scientific CR800 logs data. A calibration

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curve is generated for each TCs by logging temperature of boiled water as it cools as

represented in Equation 2-2.

( ) ⁄ (2-2)

In this equation, TC is the thermocouple reading (°C) and Tt is the temperature (°C)

recorded using an alcohol thermometer. Runoff is measured using a 25.4mm (1 inch)

calibrated Parshall flume. Flow depth is measured using a 24 volt ultrasonic sensor and

recorded. From the calibration the relationship between flow (Q) and depth in the flume

is given in Equation 2-3, for Q (L/s) and D, depth in the flume (inches). Intra-event TC

data are logged at five second intervals.

(2-3)

Calculation Methods for Temporal Distribution of Heat Transfer to Runoff During Event

NHT from the pavement to the runoff is calculated by the convection equation (Herb et

al. 2008) as shown in Equation 2-4 where qc is the pavement net heat export to runoff

(W/m2), is the runoff temperature ( ), is the dewpoint temperature ( ), as a

surrogate for rainfall temperature (U.S. Army Corps of Engineers, 1956), is the flow

(m3/s), is the specific heat of runoff (J/kg-K), is the runoff density (kg/m3), and As

is the contributing area (m2). The Kolomogorov-Smirnov (K-S) test is performed for

goodness of fit between cumulative runoff volume and cumulative NHT to the runoff.

This test is chosen due to the non-normal distribution of intra-event flows.

( ) (2-4)

A heat-based first flush is defined as an event where there is a disproportionate

heat transfer as NHT (analogous to mass) in relation to runoff volume early in the event.

In contrast, a flow limited event is an event in which NHT is proportional to flow; heat

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transferred to runoff is linearly proportional to flow volume. A temperature-based first

flush is defined where there is a disproportionate increase in runoff temperature

(analogous to concentration) in relationship to runoff volume early in the event, followed

by a rapid decline in runoff temperature.

Method Components of Heat Balance Models

Simulation using heat balance models requires pavement characterization, atmospheric

data, and pavement and runoff temperature data during a storm event. The models are

validated by comparing intra-event modeled results to measured NHT. Heat balance

model components are utilized from Janke et al. (2009), Herb et al. (2008), Van Buren

et al. (2000), Kim et al. (2008), Thompson et al. (2008), and Sansalone and Teng

(2005). Models incorporating these components are compared with a heat budget on

rainfall-runoff generated from measured rainfall and runoff temperatures. The governing

heat balance equations used in this study are shown in Equation 2-5 for the Van Buren

et al. method (2000) and in Equation 2-6 for the other methods. In these equations, qt is

the total heat stored in the pavement. Thompson et al. (2008) further includes

pavement-subgrade conduction (qsub) as a loss term. All balances are in W/m2. Table

2-1 presents thermal properties based on published results.

– , ( )- (2-5)

(2-6)

Radiation

Net radiation qrad may be calculated as shown in Equation 2-7 where qr,s is net

direct and diffuse solar radiation where qr,lw is net longwave radiation (W/m2). Solar

radiation is calculated in the same manner for each method, shown in Equation 2-8.

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(2-7)

qr,s = rs(1-α) (2-8)

In Equation 2-8, rs is the total incoming solar radiation at the surface (W/m2) and α is the

albedo. In contrast to solar radiation, methods for net long wave radiation are more

variable. Janke et al. (2009) calculates net longwave radiation as summarized in

Equations 2-9 and 2-10.

(

) (2-9)

(

) (2-10)

In these equations is amospheric emissivity, is cloud cover fraction, is surface

emissivity, Ta,k is air temperature (K), Ts,k is surface temperature (K), es,kPa is saturated

vapor pressure (kPa), and is the Stefan-Boltzmann constant (J1K-4m-2sec-1). Net

longwave radiation from Herb et al. (2008) is summarized in Equation 2-11 where ea,Pa

is surface vapor pressure (Pa). Kim’s longwave radiation is shown in Equation 2-12

where ea,Hg is surface pressure (mm Hg).

( ( )

) (2-11)

( √ ) (2-12)

Equation 2-13 shows the calculation method for Sanalone and Teng (2005) where

atmospheric emissivity is calculated as shown in Equation 2-14, where is the

vapor pressure at 2 meters (mbar).

( )( )

(2-13)

. (2-14)

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Heat loss by evaporation

Evaporative heat loss model components vary across studies. Van Buren’s method is

summarized in Equations 2-15, 2-16, and 2-17. In these equations, r is runoff water

density (kg/m3), and Dv are the latent heat of vaporization (J/kg) and evaporation rate

(m/s), Tr is runoff temperature (°C), is wind speed (m/s), and RH is relative humidity.

Herb et al. (2008) utilizes Equation 2-18.

(2-15)

, ( )- (2-16)

( ) ( ) (2-17)

( )( ) (2-18)

In Equation 2-18, is the air density (kg/m3), and are published without

reference to units, is the difference in virtual temperature between the surface and

air (°C) (Ryan et al. 1974), and q is specific humidity (kg/kg). Virtual temperature is the

equivalent dry air temperature if pressure and density equal measured moist air

conditions. Specific humidity is shown in Equation 2-19.

.

/ (2-19)

In this expression qx is either the saturated or surface specific humidity, is saturated

or surface vapor pressure and p is atmospheric pressure, all of the same units. Kim et

al. (2008) report heat loss by evaporation to be a function of wind speed and vapor

pressure. The heat loss equation is derived from the form discussed in Edinger (1974)

as shown in Equation 2-20.

( )( ) (2-20)

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Kim et. al. present the following values for wind function coefficients: a0 = 57; a2 = 2.85.

Thompson et al. (2008) publish a similar expression shown in Equation 2-21.

( )( ) (2-21)

In this equation ao = [7.2 to 13.6], a1 = [3.1 to 4.9], a2 = [0.0 to 0.66], and es,Hg is in mm

Hg. An alternative method (Sansalone and Teng 2005) is based on Penman-Monteith

(Monteith 1980).

Sensible heat loss

Sensible heat loss is explicitly added to the heat balance by Van Buren et al., Herb

et al., and Kim et al. Van Buren et al. calculate sensible heat as a function of

evaporation by multiplying by the Bowen ratio as shown in Equation 2-22.

[ (

( ))] (2-22)

In this expression is atmospheric pressure in kPa, and temperature is recorded in

°C. This ratio is also used to calculate sensible heat loss as a function of qevap using the

Sansalone and Teng method (2005). Herb et al. utilize Equation 2-23 to calculate heat

transfer by sensible heat.

( )( ) (2-23)

In this expression is the specific heat of the air (1.005 J/kg-K) and Ts is surface

temperature (°C). Kim et al. use a similar method shown in Equation 2-24.

( )( ) (2-24)

In this expression, c1 is Bowen’s coefficient, equal to 0.47mm Hg/°C.

Heat loss by convection

Convection is calculated as the remainder of the heat balance equation and does

not include heat loss of evaporation or sensible heat; hence it is defined as net heat

26

transfer (NHT). Results are compared to values calculated explicitly using the rainfall-

runoff temperature differential method described previously. Figure 2-4 demonstrates

the heat balance. The measured NHT response is adjusted by the storm’s average

pavement residence time to better correlate runoff temperature readings with NHT

calculated by pavement response. The methodology by which convection is solved for

in the heat budget is as shown in Equations 2-25 and 2-26, written to express heat gain

by radiation and heat loss by other terms.

(2-25)

Tpavi+1 = Tpavi + ( )*

( ) (2-26)

Substitution of Runoff Temperature for Pavement Surface Temperature

Herb et al. and Janke et al. indicate that turbulence generate a uniform runoff

temperature equal to pavement temperature at the start of a given time step. Therefore

this study examines if substituting runoff temperature for pavement surface temperature

impacts model predictions. Results from the substitution of runoff temperature for

pavement surface temperature are compared to results from the same events where the

models do not substitute runoff temperature for surface temperature.

Results and Discussion

Heat Transfer to Runoff during an Event

Table 2-2 summarizes event data while Table 2-3 summarizes correlation

coefficients between storm event parameters. There is a positive correlation (r = 0.96)

between peak flow and NHT. The correlation with NHT for rainfall is 0.64; for initial air

temperature is 0.14; and for continuous flow duration is 0.24. Table 2-2 illustrates the

27

positive correlation between peak flow and NHT reflected by the K-S test for similarity

between cumulative flow and NHT in 12 of 17 events.

Impact of hydrologic parameters on heat transfer

Figures 2-5 and 2-6 illustrate relationships between NHT and runoff volume for low

and medium flow storms as defined in Table 2-4. K-S tests between cumulative runoff

volume and cumulative NHT indicate a statistically significant difference (p > = 0.05).

While these events illustrate a temperature first-flush, with respect to NHT both events

are flow limited with respect to thermal load. There is a linear relationship between

cumulative NHT and volume. The net flux of heat to runoff continues throughout each

event and dilution occurs during peak flows. Instantaneous NHT and instantaneous

flow follow similar temporal patterns, suggesting lack of a distinct heat based first flush.

In contrast, Figure 2-7 illustrates the only heat limited event (Tropical Storm, TS Fay) in

the database, where cumulative heat transfer proceeds cumulative flow. The maximum

difference between cumulative runoff and cumulative NHT is 33.2% (p < = 0.05). All

other events are flow limited where heat is not exhausted from the pavement.

Of the 17 storms, only five produce a significant difference in trajectories between

cumulative flow and NHT as shown in Table 2-2. For the remaining 12 storms,

cumulative NHT shows an approximate linear trajectory when plotted against

cumulative flow as shown in Figure 2-8. Results indicate that hydrology drives NHT for

a given pavement source area. Relative heat transfer (RHT, defined as NHT divided by

rainfall depth) is conceptually similar (ignoring losses) to an event mean concentration

(EMC); in this case, dividing NHT by rainfall depth is similar to dividing constituent load

by runoff volume. Results in Figure 2-8 indicate for high intensity events, there is a

28

lower RHT and by proxy a lower unit heat transfer as compared to the short duration,

lower flow events. The negative correlation between MPRT and NHT indicates that

events with longer pavement residence time have lower NHT from pavement to runoff.

Parameters other than hydrologic parameters have the potential to influence NHT

and RHT. Correlations for RHT are defined as follows: no correlation, r ≤ 0.2; weak

correlation, r ≤ 0.5; and correlated, r > 0.5. Based upon analysis of the 17 measured

events, tabulated in Table 2-3, initial radiation levels show no correlation with NHT (r =

0.05). However, Figure 2-7 is an example where solar radiation between rainfall bands

of TS Fay results in pavement temperature increasing despite moderate wind during the

storm. Wind speed before the onset of rainfall is observed to have no correlation with

RHT (r = 0.08) but does have a moderate negative correlation with NHT (r = -0.48). In a

separate experiment, air flow over the surface of 38mm thick asphalt at 2.2 m/s resulted

in 6% drop in surface temperature but 11% in the pavement interior, after 8 minutes of

airflow as shown in Figure 2-12. This suggests that wind does affect surface

temperature, however with a corresponding slow rate of interior heat loss, supporting

the moderate correlation with NHT measured in-situ. Results illustrate that antecedent

air temperature (immediately before rainfall) exhibits a weak correlation with RHT (r =

0.42).

Relationship between antecedent pavement temperature and heat transfer

Antecedent asphalt temperature correlates with RHT (r = 0.74) more strongly than

with NHT (r = 0.45) and has the greatest correlation of any non-hydrologic factor for

NHT and RHT. Antecedent subgrade temperature has a weak correlation with NHT (r =

0.25) and RHT (0.28), noting that subgrade is buffered from surface temperature and

hydrologic parameters. Results indicate that initial concrete temperatures are lower

29

than asphalt and subgrade. As a reflective surface, concrete does not correlate

strongly with either NHT or RHT.

Impact of event date and start time on heat transfer

There is a weak correlation between event date and heat transfer, as between

event date and other initial conditions (air, subgrade, and pavement temperature).

Similarity of the intra-event phenomena at different seasonal points suggests a lack of

seasonal correlation. Event start time has little correlation with NHT (r = -0.16) or RHT (r

= 0.04). Results shown in Table 2-4 suggest that shading of locations A1 and A5

confounds any correlation between event date and pavement temperature patterns.

This may also cause the difference in East Concrete and West Concrete pavement

temperatures shown in Figures 2-5 through 2-7.

Heat Balance Model Comparison

Table 2-5 summarizes results of cumulative net heat transfer (KJ/m2) measured

directly by heat gain in runoff as well as modeled using the heat transfer components

from Sansalone and Teng, Herb as modified to use Janke’s qlw (hereafter modified

Herb), Van Buren, Kim, Kim as modified to use Sansalone and Teng’s qlw (hereafter

modified Kim), Kim modified to use Thompson’s qv (hereafter Thompson), and Kim

modified both to use Thompson’s qv and Sansalone and Teng’s qr,lw (hereafter modified

Thompson). Additionally, all events are modeled with the substitution of runoff

temperature for pavement surface temperature.

Figures 2-9 and 2-10 summarize modeling results for four storms where pavement

surface temperature is measured. The two closest fitting models are shown. In

addition, these figures also summarize the mean differential produced by the two

30

closest models when runoff temperature (Tro) is substituted for pavement surface

temperature (Tsurf) in each model; assuming Tro at the discharge location equals Tsurf.

The rationale for applying net longwave radiation from Janke et al (2009) in the

Herb model is two-fold: (1) when applying qlw as calculated by Herb, the net flux of

longwave radiation away from the pavement is lower under clear sky conditions than

under cloudy conditions; (2) the Boltzmann constant is reported in non-standard units in

the Herb model, possibly leading to modeling error. The Janke method for calculating

qlw is of similar origin to the Herb method and provides results consistent with

Sansalone and Teng (2005).

The Kim et al. (2008) method for calculation of net longwave radiation has been

modified to substitute Sansalone and Teng’s qlw because Kim et al. refer to longwave

radiation leaving the water surface but provide no equation for calculation. Results

calculated without this term are opposite in sign and 10x the magnitude of the

Sansalone and Teng (2005) and the Janke et al. (2009) methods as shown in Table 2-

5. The Thompson model is very similar to the Kim model but presents a different

calculation method for evaporative heat transfer. The same longwave radiation

modification made to the Kim model is applied to the Thompson model.

The distribution of residuals in Figure 2-11 illustrate that both the modified Kim and

the Sansalone and Teng methods represent measured data (mean normalized

residuals closest to 0). For example, the 14 July event is best represented using the

modified Kim method. This method is also closest to measured total NHT for the 12

August event, followed by the Sansalone and Teng method. The 10 September event

is also best predicted using the same methods. In contrast, the modified Herb method

31

over-predicts NHT and the Thompson model under-predicts NHT for the measured

events. During the 12 August event, all models generate a greater magnitude increase

in heat transfer during peak flow (5 L/s) than measured values. The 21 August event

has a very low instantaneous NHT and all models perform poorly. There is a difference

in calculated NHT when substituting runoff temperature for asphalt surface temperature

as shown in Figure 2-9 however the difference is relatively small. The maximum

differences for each of the four events are -16.7, -19.9, 4.1, and -41.8 W/m2 for the 14

July, the 12 August, the 21 August and the 10 September events, respectively. The

mean differences for the same storm events are -0.7, -2.1, 2.3, and -4.52 W/m2.

Discussion

Results of this thermal pollution study for an asphalt-paved source area illustrate a

temperature first-flush and lack of a heat-based first flush. This finding suggests that

thermal pollutant transport can be analogous to particulate or solute transport from

urban source areas (Sheng et al. 2008). Sheng et al. also suggest that there is a need

to capture and treat the entire event rather than a first flush or water quality volume

(WQV) that is designated a-priori. This link between hydrology and pollutant transport is

also supported by the correlation between NHT and rainfall-runoff flow volume and by

the statistical analysis of the same.

Results demonstrate that pavement temperature exhibits a strong correlation with

NHT. For the same ambient conditions, low rainfall depth events can exhibit a more

significant temperature increase in runoff than high rainfall depth events for asphalt-

paved source areas. However, for the same ambient conditions the NHT for a high

rainfall depth event will be greater than a low rainfall depth event. In contrast to

capturing a first-flush or WQV, a more effective management strategy may be to

32

minimize the storage of heat in the pavement through design and material changes.

This strategy also remedies the disproportionate impact of thermal pollution on

perennial, low volume, or ephemeral systems compared to streams with significant base

flow. Radiation is the dominant mechanism by which the pavement warms; hence,

although a low correlation is measured between radiation and NHT/RHT, it is

particularly useful to minimize radiation that reaches or is absorbed by pavement. For

example, the uses of shading and concrete pavement have well-known thermal benefits

and are passive strategies.

The thermal discontinuity between the subgrade (composed largely of sand) and

the asphalt is shown clearly in Figure 2-6. The implications of a thermal disconnect are

multi-fold. It suggests that models do not need to focus on sub pavement heat content;

at the same time, it implies that better coupling may be achieved by using engineered

pavement and ground media to enhance thermal connectivity between the pavement

and the subgrade.

There are multiple mechanisms that impact the temperature of receiving waters

due to urbanization. The critical component of thermal pollution in urban streams is

direct discharge. While there are deviations between the Sansalone and Teng,

modified Kim, and modified Herb models, all of the aforementioned models are

observed to approximate measured NHT following the same temporal pattern. Results

suggest that existing models may benefit by performing more tests under real storm

events, validating parameters such as longwave radiation with measured values, and

focusing more discretely on evaporation early in the storm event.

33

Substitution of runoff temperature for pavement surface temperature provides

cumulative NHT values that compare nearly as closely to measured surface

temperature as non-substituted NHT calculation. However, initial runoff temperature

misrepresents initial pavement temperature because it is cooler than the asphalt

pavement (Figures 2-5 through 2-7). It is important to accurately model initial heat

transfer because of the rapid convection and evaporation processes unique to event

beginnings.

Summary

Thermal load transport in runoff from urban asphalt pavement is measured for 17

events at a Gainesville, FL catchment and results are simulated with a series of

published models. Hypothesizing that thermal load delivery is driven by hydrology and

is primarily flow limited, a K-S statistical analysis is performed that demonstrates that for

12 out of 17 storms normalized cumulative runoff is an appropriate surrogate for

normalized cumulative NHT. Correlation results between these parameters also

support this conclusion. The thermal load transport is predominately flow limited with no

first-flush in relation to NHT. While pavement temperature is strongly correlated to

NHT, results indicate that seasonal event date, event duration, and antecedent weather

parameters are not correlated to NHT.

Results do not support the hypothesis that pavement heat conduction is an

appropriate estimation of heat transfer to and from the pavement based on measured

pavement and pavement subgrade temperatures during runoff events. Governing

equations for pavement heat balance models described by Herb et al. (2008) and Kim

et al. (2009) are applied in this study and evaluated with measured NHT. These models

are modified to include heat balance components from Janke et al. (2009), Sansalone

34

and Teng (2005), Thompson et al. (2009) and Van Buren et al. (2009). Results indicate

heat transfer is modeled equally well with more than one model but that the heat

transfer predicted by each model early in an event requires further refinement.

Utilization of runoff temperature as a surrogate for asphalt surface temperature has little

effect on simulated NHT based on models presented but provides a lower NHT early in

the event.

35

Table 2-1. Selected properties of asphalt pavement from various studies

Study Density

(kg/m

3)

Thermal Conductivity

(W/m-oC)

Specific Heat

(J/kg-oC)

Thermal Diffusivity

(m2/s)

Albedo Emissivity

Van Buren et al. (2000)

2250 (1760)

1.21 (1.3)

921 (837)

5.86x10-7

(8.79x10-7

) NR NR

Janke et al. (2009)

2100-2400 (1300-1500)

1.4-1.8 (0.4-1.2)

1120-1370 (900-1400)

NR 0.12 0.94

Herb et al. (2008)

NR NR NR 4x10

-7

(6x10-7

) 0.12 0.94

Kim et al. (2008)

NR NR NR 6.98x10-7

0.05 NR

This Study 1850 1.3 (0.6) 1050 6.69 x10-7

0.12 0.94

Note: Values in parentheses are for pavement subgrade. NR: not reported

36

Table 2-2. Storm event data for measured rainfall events and Kolomogorov-Smirnov (K-S) test for goodness of fit between normalized cumulative heat and time and normalized cumulative flow and time

Event D

ate

(200

8)

(MM

-DD

)

Sta

rt T

ime

of R

ain

fall

(HH

:mm

) (t

o)

Dura

tion (

H:m

m)

Rain

fall

(mm

)

Peak F

low

(L/s

)

Initia

l A

ir

Tem

pera

ture

(oC

)

Initia

l P

avem

ent

Tem

pera

ture

(oC

)

Initia

l te

mpera

ture

of soil(

oC

)

Runoff

Tm

ax (

oC

)

Continu

ous F

low

Dura

tion (

H:m

m)*

Pre

vio

us D

ry H

ours

Net H

eat T

ransfe

r to

Runoff

(K

J)

Rela

tive H

eat

Tra

nsfe

r

(KJ/m

m o

f ra

infa

ll)

MP

RT

** (

min

)

D (K-S test), P

++

7-31 10:59 0:42 1.27 .15 30.6 33.6 29.1 32.5 0:04 37 2,035 1,602 4 0.044,1 7-14 22:11 1:19 2.03 .15 27.2 31.2 28.7 27.5 0:28 75 3,785 1,865 6 0.033,1 10-23 14:58 0:51 3.56 1.6++ 25.6 28 24.9 26.5 0:15 340 19,216 5,398 3 0.3, 0.043 (n) 6-22 14:38 2:25 1.78 0.07 31.7 33.2 28.3 31.0 0:06 25 2,248 1,263 5 0.283, ~0.0 6-3 15:26 0:55 2.03 0.82 33.9 39.3 29.9 34.2 0:15 600 14,814 7,298 4 0.l22, 0.832 9-20 13:44 0:47 3.30 1.01 27.8 36.5 28.4 30.3 0:16 45 15,055 4,562 3 0.0857, 0.99 8-21** 12:34 7:09 54.6 5.94

++ 26.1 27.2 28.1 27.8 2:47 2 74,700 1,368 2 0.332, ~0.0

10-09 14:08 1:41 20.8 9.2++

29.4 31.6 26.7 26.8 0:26 20 131,048 6,300 3 0.40, ~0.0 8-12 14:29 1:30 16.8 4.6 27.8 31.3 30.3 28.7 1:10 2 45,771 2,724 5 0.0737, 0.951 6-30 14:42 0:31 5.58 3.17 30.0 38.9 27.4 32 0:13 45 39,277 7,039 4 0.111, 0.994 6-11 13:22 1:54 21.6 11

++ 29.4 41.7 29.4 33.4 0:30 12 218,622 10,121 0.5 0.351, ~0.015

7-15 13:08 1:40 62.2 13.2++

29.4 35.7 28.6 31.1 0:54 12 170,047 2,734 1 0.180, 0.514 9-10 16:13 0:58 6.10 1.96++ 32.8 37.4 29.8 31.1 0:42 120 38,022 6,233 3 0.204, 0.19 6-10 14:02 1:21 22.6 10.7++ 32.8 42.2 31.5 31.7 1:00 600 195,427 8,647 4.5 0.0405, 1.0 7-29 11:43 0:43 5.08 3.64++ 31.1 37.8 30.6 32.8 0:25 330 45,930 9,041 5 0.18, 0.51 6-21 11:45 1:10 13.7 3.8 30.0 27.3 28.1 26.4 0:10 61 35,808 2,614 3 0.0465, 1 (n) 6-23 10:35 2:27 7.87 0.52 25.6 28.2 28.1 0.52 1:30 18 26,022 3,306 3 0.0417, 1

* Excludes gutter flow; **MPRT (Median Pavement Residence Time); ++

(n) = normally distributed; D is maximum difference, P is p-value for test of significant difference where α = 0.05.

37

Table 2-3. Correlations between storm event parameters. Note that correlation coefficients for wind velocity and radiation are not shown.

Eve

nt

Da

te

Sta

rt o

f R

ain

fall

(t o

)

Dura

tio

n o

f E

ve

nt

Rain

fall

De

pth

Pe

ak F

low

An

tece

den

t A

ir T

em

pera

ture

An

tece

den

t A

sp

ha

lt

Te

mp

era

ture

An

tece

den

t S

ubg

rad

e

Te

mp

era

ture

Ma

xim

um

Ru

no

ff T

em

pe

ratu

re

(Tm

ax)

Con

tin

uo

us F

low

Dura

tion

(CF

D)

Pre

vio

us D

ry H

ou

rs (P

DH

)

NH

T (K

J)

RH

T (

J/m

m r

un

off

)

MP

RT

(m

inu

tes)

Event Date 1.00 Start of Rainfall (to) 0.08 1.00

Duration of Event 0.02 -0.17 1.00 Rainfall Depth 0.00 -0.23 0.62 1.00

Peak Flow -0.09 -0.19 0.18 0.77 1.00 Air T (to) -0.41 0.03 -0.39 -0.20 0.09 1.00

Asphalt T (to) -0.37 0.09 -0.42 -0.09 0.33 0.69 1.00 Subgrade T (to) -0.52 -0.03 -0.10 0.02 0.17 0.58 0.59 1.00

Runoff Tmax 0.01 0.25 -0.22 0.04 0.22 0.58 0.56 0.29 1.00 CFD 0.04 -0.19 0.85 0.66 0.27 -0.43 -0.32 0.11 -0.37 1.00

PDH -0.17 0.09 -0.10 -0.11 0.02 0.43 0.36 0.33 0.25 0.00 1.00 NHT -0.18 -0.16 0.15 0.64 0.96 0.14 0.45 0.25 0.19 0.24 0.08 1.00

RHT -0.10 0.04 -0.56 -0.16 0.36 0.42 0.74 0.28 0.29 -0.24 0.49 0.49 1.00 MPRT -0.13 0.44 -0.33 -0.53 -0.50 0.22 -0.01 0.28 0.09 -0.27 0.23 -0.53 -0.19 1.00

Note: Units are as defined in the previous table. MPRT = Mean Pavement Residence Time; Tmax Runoff = Maximum Runoff Temperature

38

Table 2-4. Tabular pavement and subgrade temperature profiles at beginning and end of storm.

Event Date (2008) (MM-DD)

Initial Pavement

Profile

Final Pavement

Profile

Initial Subgrade

Profile

Final Subgrade

Profile

Start Time

(HH:mm)

Runoff Volume

(L) Q50

(L/s) Percentile

(%)

6-10 3>5>1 3>5>1 3>5>1 3>5>1 14:00 8000 1.195 75-100 6-21 3>5>1 3>5>1 3>5>1 3>5>1 11:40 3568 0.391 0-25 7-29 3>5>1 3>5>1 3>5>1 3>5>1 11:42 1406 0.656 75-100 7-31 3>5>1 3>5>1 3>5>1 3>5>1 10:56 66 0.061 0-25 7-15 3>5>1 3>5>1 3>5>1 3>5>1 13:03 22380 3.451 75-100 7-14 3>5>1 3>5>1 3>1>5 3>1>5 21:25 248 0.005 0-25 8-21 3>5>1 3>1>5 3>5>1 3>5>1 11:05 20409 0.310 50-75 6-23 3>5>1 3>1>5 3>1>5 3>1>5 10:35 1373 0.184 25-50 6-11 3>1>5 3>5>1 3>5>1 3>5>1 13:11 6678 1.560 75-100 6-22 3>1>5 3>5>1 3>1>5 3>1>5 14:33 29 0.006 0-25 9-20 3>1>5 3>5>1 3>1>5 3>1>5 13:36 502 0.200 25-50 10-23 3>1>5 3>5>1 3>1>5 3>1>5 14:50 916 0.194 25-50 6-3 3>1>5 3>1>5 3>1>5 3>1>5 15:25 293 0.073 0-25 6-30 3>1>5 3>1>5 3>1>5 3>1>5 14:38 1028 0.359 50-75 8-12 3>1>5 3>1>5 3>1>5 3>1>5 14:24 3861 0.216 25-50 10-9 3>1>5 3>1>5 3>1>5 3>1>5 13:56 8467 0.707 75-100 9-10 1>3>5 3>1>5 1>3>5 3>1>5 16:07 1540 0.217 50-75

Note: Thermal profiles are in order from hot to cold. Thermal profile symbols are 1=A1, 2=A2, 3=A3 as illustrated in Figure 2-3. The 25, 50, 75th percentile = 0.184, 0.217, 0.656 L/s, respectively. Flow less than 25% is defined as low flow; less than 75% is moderate flow; greater than or equal to 75% is high flow. Table 2-5. Total NHT for various modeling methods compared to measured values.

Negative values represent heat gain by pavement.

Event Date (Day/Month/2008) 6/10 6/23 7/14 8/12 8/21 9/10 Model Components Heat Transfer to Runoff (KJ)

Sansalone and Teng -28 54 34 104 -209 101

Modified Herb 63 214 94 134 157 122

Van Buren -377 -411 -122 -69 -290 -198 Kim -909 2014 684 653 3770 941 Thompson -1025 2021 662 610 3497 895

Modified Kim -77 -54 19 99 -151 83 Modified Thompson -192 -46 -4 56 -425 37

Measured 258 68 9 83 51 76

39

Month

Januar

y

Feb

ruar

y

Mar

ch

Apri

l

May

June

Mea

n M

onth

ly P

reci

pit

atio

n (

in)

0

2

4

6

8

Mea

n M

onth

ly A

ir T

emper

atu

re (

oC

)

0

4

8

12

16

20

24

28

Month

January

Febru

ary

Marc

h

April

May

June

Mea

n N

um

ber

of

Even

ts p

er M

onth

0

10

20

30

40

50# Events / Month

Mean Monthly Precipitation

Air T

Portland, ORGainesville, FL

Figure 2-1. Historical monthly distribution of weather data for Gainesville, FL from

August 1998 to July 2008 (NCDC, 2009) and for Portland, OR (Oregon Climate Service, 2010) from January 1998 to December 2008.

Figure 2-2. Lake Alice watershed including subject catchment (~450 m2).

40

Figure 2-3. Plan and cross-sectional view of thermocouples (TC) for catchment

pavement system in Lake Alice watershed.

Figure 2-4. Conceptual pavement heat balance model with nominal thermocouple

installation depths. Tdew represents rainfall temperature (oC); TR.O. is runoff

temperature (oC); qr,lw is net longwave radiation; qr,s is net shortwave radiation; qconv is convective heat transfer; qv is evaporative heat transfer; qs is sensible heat transfer; Tsurf is surface temperature (

oC); T13 is asphalt temperature (oC) measured at ~13mm depth; T38 is asphalt temperature (

oC) measured at ~38mm depth; Tsub is subgrade temperature (

oC) measured at ~76mm depth; Tpav is average pavement temperature.

41

Q, L

/s

0.0

0.2

0.4

0.6

Tem

per

atu

re (

oC

)

22

24

26

28

Win

d (

m/s

)

0

2

4

6

8Q

TQ

Air

Wind

23 June 2008to = 10:35:00

Incr

emen

tal

Hea

t T

ran

sfer

(W

/m2

)

0

10

20

30

40

50

% l

ess

than

, fo

r (H

eat,

V) n

0.0

0.2

0.4

0.6

0.8

1.0

Rad

iati

on

(W

/m2

)

0

100

200

300

400

500

Heat

V

Heat

Radiation

Elapsed Time, HH

00:0

0

00:3

0

01:0

0

01:3

0

02:0

0

Tem

per

atu

re (

oC

)

23

24

25

26

27

28

29Mean Pavement T

Mean Subgrade T

E. Concrete T

Runoff

Figure 2-5. Low flow rate storm event data recorded on June 23, 2008. Q: Flow; V:

Volume; T: Temperature OC; Heatn: normalized heat; Vn: normalized volume

42

Q (

L/s

)

0

1

2

3

4

5

Tem

per

ature

(o

C)

22

24

26

28

30

32

34W

ind

(m

/s)

0

2

4

6

8

10

Q

TQ

Air

Wind

30 June 2008

to = 14:38

Incr

emen

tal

Hea

t T

ransf

er (

W/m

2)

X10

0

5

10

15

20

25

30

% l

ess

than

, fo

r (H

eat,

V) n

0.0

0.2

0.4

0.6

0.8

1.0

Rad

iati

on

(W

/m2

)

0

100

200

300

400Heat

V

Heat

Radiation

Elapsed Time, HH:mm

0

0:0

0

0

0:1

5

0

0:3

0

Tem

per

ature

(o

C)

25

30

35

40

45Mean Pavement T

Mean Subgrade T

E. Concrete T

Runoff

Figure 2-6. Moderate flow rate storm event data recorded on June 30, 2008. Q: Flow; V:

Volume; T: Temperature OC; Heatn: normalized heat; Vn: normalized volume

43

Q, L

/s

0

2

4

6

Tem

per

atu

re (

oC

)

24

25

26

27

28

Win

d (

m/s

)

2

4

6

8

10Q

TQ

Air

Wind

Elapsed Time, HH:mm

0

0:0

0

0

1:0

0

0

2:0

0

0

3:0

0

0

4:0

0

0

5:0

0

0

6:0

0

0

7:0

0

0

8:0

0

Pav

emen

t T

emp

erat

ure

(o

C)

24

25

26

27

28Mean Pavement T

Mean Subgrade T

E. Concrete T

Incr

emen

tal

Hea

t T

ran

sfer

(W

/m2

)

0

10

20

30

40

50

% l

esst

han

, fo

r (H

eat,

V) n

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Rad

iati

on

(W

/m2

)0

20

40

60

80

100Heat

V

Heat

Radiation

21 Aug 2008t0 = 11:05:00

Figure 2-7. Storm event data recorded on August 21, 2008 (Tropical Storm Fay). Q:

Flow; V: Volume; T: Temperature OC; Heatn: normalized heat; Vn: normalized volume

44

Cumulative Flow Volume (L)

0 5000 10000 15000 20000

Cum

ula

tive

Hea

t T

ransp

ort

ed (

KJ)

0.0

5.0e+4

1.0e+5

1.5e+5

2.0e+5

6-30

6-23

8-12

7-31

7-29

7-15

7-14

9-10

6-10

6-03

9-20

6-21

Figure 2-8. Distributions of cumulative heat and cumulative flow for 12 storms that are

similar according to K-S tests of difference between normalized values of the former. The heat response is stronger during small storms and shallow under larger events, with the exception of the 6-10 event.

45

Storm Duration (HH:mm)

00:10:00 00:20:00 00:30:00 00:40:00

Flo

w (

L/s

)

0

1

2

3

4

5

6N

et H

eat

Tra

nsf

er (

W/m

2)

-100

0

100

200

300

400

500

Rad

iati

on

an

d (

W/m

2)

Mo

del

Div

erg

ence-20

02040

Flow

Measured

Modified Kim

Modified Thompson

Radiation

DNHT

14 July, 2008

A

Storm Duration (HH:mm)

00:00 00:10 00:20 00:30 00:40 00:50F

low

(L

/s)

0

1

2

3

4

5

6

Net

Hea

t T

ran

sfer

(W

/m2

)

-100

0

100

200

300

400

500

Rad

iati

on

(W

/m2

)an

d M

od

el D

iver

gen

ce

-25

0

25

50Flow

Measured

Sansalone

Modified Kim

Radiation

DNHT

B Figure 2-9. Modeled storm event data showing only best fit models for A) 14 July 2008,

B) 12 August 2008. ΔNHT is the difference between heat transfer modeled using a substitution of runoff temperature for pavement surface temperature.

46

Storm Duration (HH:mm)

01:00 03:00 05:00 07:00

Flo

w (

L/s

)

0

1

2

3

4

5

6N

et H

eat

Tra

nsf

er (

W/m

2)

-100

0

100

200

300

400

500

Rad

iati

on

(W

/m2

)M

od

el D

iver

gen

ce0204060

Flow

Measured

Sansalone

Modified Herb

Radiation

NHT

21 August, 2008

A

Storm Duration (HH:mm)

00:00 00:10 00:20 00:30 00:40 00:50

Flo

w (

L/s

)0

1

2

3

4

5

6

Net

Hea

t T

ransf

er (

W/m

2)

-200

-100

0

100

200

300

400

500

Rad

iati

on a

nd (

W/m

2)

Mod

el D

iver

gen

ce-40-2002040

Flow

Measured

Sansalone

Modified Kim

Radiation

DNHT

September 10, 2008

B Figure 2-10. Modeled storm event data showing only best fit models for A) 21 August

2008, and B) September 10 2008. ΔNHT is the difference between heat transfer modeled using a substitution of runoff temperature for pavement surface temperature.

47

No

rmal

ized

Res

idu

als

-4

-3

-2

-1

0

1

2

3

No

rmal

ized

Res

idu

als

-4

-3

-2

-1

0

1

2

3

Elapsed Time (HH:mm)

00:00 00:15 00:30 00:45

Elapsed Time (HH:mm)

00:00 00:15 00:30 00:45

Norm

aliz

ed R

esid

ual

s

-6

-4

-2

0

2

Modified Herb et al.

Mean Modified Herb

Sansalone and Teng

Mean Sansalone

Modified Kim et al.

Mean Modified Kim

Modified Thompson et al.

Mean Modified ThompsonRunoff Temperature Measurements

Measured Pavement Surface TemperatureMeasured Pavement Surface Temperature

Measured Pavement Surface TemperatureMeasured Pavement Surface Temperature

Figure 2-11. Residual values for four models. Kim and Thompson models are corrected

to use qr,lw from Sansalone and Teng model. Herb is modified to use qr,lw from Janke model. A mean of 0 with a normal distribution about the mean indicates a close estimation of total heat transfer with a good fit to the measured NHT. Use of runoff temperature in place of pavement surface temperature for NHT model calculations results in trending similar mean residual values.

48

Time (minutes)

0 2 4 6 8 10

Pav

emen

t T

emper

ature

(o

C)

50

55

60

65

70

Interior Pavement (19mm depth)

Pavement Surface

Figure 2-12. Median temperature at two depths in a 38mm asphalt pavement with a

forced wind velocity of 2.2 m/s over the pavement surface. There is an 11% reduction in surface temperature and 6% reduction in the interior temperature. 95% confidence interval is shown in light-gray for surface measurements and dark gray for interior measurements.

49

CHAPTER 3 CYCLIC TEMPERATURE PROFILES FOR ASPHALTIC PAVEMENT AS A FUNCTION

OF TREE CANOPY SHADING AND VEHICULAR PARKING FREQUENCY

Background

The temperature of urban runoff is fast becoming a concern in many locations

throughout the United States, most of which have sensitive cold-water habitats

(Langford 1990; Galli 1990) and some of which exhibit fish distress (Coutant 1987;

Nakatani 1969; Paul and Meyer 2001). If not mitigated, runoff temperature can have an

impact on the ecology of receiving waters (Daufresne et al. 2004; James and Xie 1998).

The clean water act, as amended by the water quality act of 1987, has established total

maximum daily loads whereby states must identify locations where controls on thermal

discharges to waters cannot assure protection of biota in those waters. Thermal TMDLs

have been established in states ranging from the Northwest (Oregon DEQ 2008) to the

Southeast (Louisiana DEQ 2001).

Parking lot surfaces dominate the urban landscape in urban environments, making

up more than 29% of paved area in Houston and Sacramento (Akbari et al. 2003) and

between 39% and 64% of commercial areas in Olympia, Washington (City of Olympia

1994). Asaeda (1996), Celestian, and Martin (2004), and Grimmond and Oke (1999)

have demonstrated a contribution to the urban heat island effect from parking lots.

Urban drainage areas used for parking generate a thermal input into stormwater run-off

that is comparable with roadways with high speed and high intensity traffic (Hanh and

Pfeifer 1994).

Low impact development best management practices (BMP) mitigate thermal load

to receiving waters in addition to meeting other stormwater criteria or ancillary benefits

such as metal, nutrient, or volumetric reduction, or even energy production (Golden

50

2007). One such BMP is to reduce the area dedicated to parking. Most municipalities

maintain minimum parking space requirements, such as 2 spaces per single family

home, 0.25 spaces per movie theater seat or 6.8 spaces per 100m2 of health spa

leasable area (Davidson and Dolnick 2002). Some requirements vary wildly between

regions or municipalities. A pool hall may vary between 1 space per billiard table in

North Ogden, Utah to 4 spaces per table in Platte County, Missouri (Litman 2006).

There also is a very complex relationship between available parking and

patronage (Shoup 1997) and few definitive numbers are available of typical parking lot

patronage (Institue for Traportation Engineers 1987). Wilson (1995) found that peak

parking demand is only 56% of total capacity at 10 office buildings in CA. According to

the Urban Land Institute, shopping malls only receive 100% parking space patronage

for 19 hours/year (Shoup, 1997). Litman (2006) produced a table from data gathered by

Gould (2003) that finds an average occupancy of

51

Scott et al. (1999) measured a 2.1-3.7°C drop in vehicle chassis temperature when

parked in shaded parking lot in Sacramento, CA, however they did not document

pavement temperature.

Objective

My study first investigates the relative impact of tree canopy shade on pavement

temperature beneath parked vehicles; the hypothesis put forth is that there exists a

demonstrable and statistically significant difference in day time pavement surface

temperature beneath a vehicle that is shaded by tree canopy and beneath a vehicle that

is not shaded. The second objective is to determine the cumulative impact of parking

activity on pavement surface temperature in a parking space under varied initial

conditions; the hypothesis put forth is that pavement exposed to insolation for 8 hours

before treatment will cool when repeatedly parking and removing vehicles over the

space while pavement that is shaded before the experiment will warm instead.

In cases where a parking lot is not filled to capacity, multiple parking spaces may

be exposed to direct solar radiation unless another form of shade is provided. The third

objective of the study is to investigate the relative influence of tree shading on roadway

temperature at the surface parking facility. The study hypothesizes that the presence of

medium to large foliage trees (as defined in McPherson et al. 2005) east and west of a

N-S road lowers peak pavement temperature and that the thermal disconnect between

asphalt and subgrade is visible as a difference in the gradient of temperature response

in the two materials.

Methodology

In my study, a student union parking lot on the University of Florida campus

located at 29.644098° N, 82.348404° W is composed of hot-mix asphaltic concrete

52

(density=1850 kg/m3, conductivity=1.3 W/m-oC, specific heat=1050 J/kg-oC, and

albedo=0.12) and is used for surface parking as shown in Figure 3-1, receiving

approximately 708 vehicles per weekday and 84 vehicles per weekend day. Two dense

foliage trees of canopy diameters > 9.1m (30ft) are located directly west and one

Magnolia Grandiflora tree (diameter >6.1m (20ft)) is located directly east of a

catchbasin that drains a 450m catchment shown in Figure 3-1. Due to the N-S

orientation of the parking spaces, most automobiles receive little to no shade from

nearby foliage. A parking stall 6m northeast of the catchbasin is shaded by the

magnolia and is used for the vehicular shade experiment.

Parking Stall Data Collection Methods

A central component of my investigation is the analysis of pavement temperature

beneath vehicles. A vehicle shade experiment is performed to determine the relative

impact of tree canopy shade on the pavement temperature beneath the vehicle.

Temperature data collection methods include point measurements of temperature taken

on the exteriors of two vehicles (on the hood, roof, and trunk) and on the pavement

beneath the vehicles as shown in Figure 3-2, on the parking space centerline, 1.22m (4

ft) interior of the front and rear of the vehicle. Parking space dimensions are measured

to be 2.74m wide by 6.1m long (9x20 ft). Type-T Omega {5TC-TT-T-30} thermocouples

(TC) are used to measure vehicle and pavement surface temperatures. TCs are

calibrated by heating water in a beaker over 30 minutes until boiling. Water

temperature is measured simultaneously using an alcohol thermometer every minute

while a datalogger measures water temperature via TCs to generate a calibration curve

for the TCs. All experimental temperature data are logged at 2 minute intervals using a

Campbell Scientific CR10x logger with AM25T multiplexer. Tests for significant

53

difference are performed using the Mann-Whitney rank sum test due to the non-normal

nature of the data.

Vehicle models used in the investigation are a 2005 Lexus RX300 (burnished gold

metallic), denoted Vehicle A, and a 2001 Toyota Corolla (silverstream opalescent),

denoted Vehicle B. Vehicles are not modified from factory condition. Temperature data

collected on 18 September and 19 September, 2010 are used to calibrate temperature

measurements including the hood, roof, trunk, and front and rear pavement

temperatures. The calibration method involves placing both vehicles in parking spaces

unobstructed from sunlight, with the front end of the vehicle facing south (same direction

as in the experimental trials), over a two day weekend period, separated by 10m to

prevent interference. Afterwards, the thermocouple readings measured on the warmer

vehicle are calibrated to the cooler readings on the other by a coefficient of

multiplication, normalizing temperatures recorded at vehicle B to those at vehicle A.

The converse method is used to normalize the cooler asphalt temperature

measurements (vehicle A) to those measured beneath the other vehicle (vehicle B).

Each of the five measurements locations is independently calibrated.

Two parking stalls are included in the shade investigation. One stall is partially

shaded from the southwest by the aforementioned magnolia tree, leaving the rear 33%

of the parking space exposed to solar radiation. An unshaded stall is located 14 meters

directly east of the shaded stall. Vehicle A is parked in the unshaded stall and vehicle B

in the shaded stall between 4 September, 2010 and 16 September, 2010. Temperature

measurements are made between 10:00 and 17:00. Upon parking the vehicle, the

54

thermocouples used to measure pavement surface temperature are affixed to the

pavement surface using thermal paste.

Simulated Driving Activity Data Collection

Three driving experiments are performed to determine the effect of engine and

drivetrain use on the pavement temperature. The first experiment is designed to

measure the impact of vehicle operation on parking space surface temperature after

being parked and shut-off. The second experiment is designed to measure the

cumulative impact on pavement temperature from parking, removing, and re