Simulation of possible scenarios for local scale energy balances in residential neighborhoods in Phoenix, AZ,

USA

Ariane Middel, Björn Hagen, Anthony Brazel, Soe Myint

Department of Computer Science, TU Kaiserslautern, Kaiserslautern, Germany School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA

Ariane.Middel@asu.edu

ABSTRACT This study addresses simulations of summertime atmospheric heating/cooling and water use at the local scale in Phoenix, Arizona – a city in the arid Southwestern United States. Our goal is to consider various climate effects by the manipulation of land coverage within a census tract size area at the local scale. This scale refers to horizontal areas of approximately 102–104 m on a side and to measurement heights in the inertial sublayer above the urban canopy and its roughness sublayer. The model we use for this scale is the Local Scale Urban Meteorological Parameterization Scheme (LUMPS) after Grimmond and Oke, 2002. We calculate different scenarios using the LUMPS model to determine the interplay of water use and diurnal variations in summer of atmospheric heating and cooling processes for selected census tracts in Phoenix. First, we simulate xeriscaping within the census tract neighborhoods by transforming green spaces into soil. The second scenario simulates an infill and Brownfield development scenario, increasing density and impervious surfaces while at the same time decreasing soil. Third, we reduce barren soil and impervious surface areas towards a green city. With LUMPS we can understand the optimization of water use and at the same time the maximization of the cooling potential within the local scale area as a whole, dependent on varying the total surface cover fractions.

Conference Topic: Built and Natural Environment Keywords: urban heat island, energy budget model, water use, planning

1. INTRODUCTION

Rapid urban growth in the Phoenix Metropolitan area has altered the Valley's surface characteristics and is expanding

the Urban Heat Island (UHI) effect (Brazel, et al., 2007). Asphalt streets, parking lots, buildings, and other sealed

surfaces store heat during the day and release it at night more slowly than vegetated areas. Consequently, night time

temperatures rise, which increases residential water use and energy demand (Guhathakurta & Gober, 2007; Golden,

Hartz, Brazel, Luber, & Phelan, 2008). Empirical studies show that UHIs also effect air quality (Cardelino, 1990), cause

human discomfort (Baker, et al., 2002) and public health issues (Harlan, Brazel, Prashad, Stefanov, & Larsen, 2006).

Increasing UHI awareness has led to the development of mitigation strategies to reduce heat islands. One solution to

mitigate the phenomenon is to increase the amount of irrigated vegetation in the urban area. Turf grasses, trees, and

shrubs store less heat during the day than heat-trapping sealed surfaces and cool their surroundings by

evapotranspiration. This cooling strategy entails an increase in the use of water for plant irrigation. Since water is a

limited resource in desert environments, it is important to find the tradeoffs between the cooling benefit of vegetation

and water demand for irrigation.

In this study, we use a simple energy budget model to explore the impacts of different vegetation strategies at a local

scale for UHI mitigation across Phoenix, AZ. Our goal is to understand the optimization of water use and at the same

time the maximization of the cooling potential within the local scale area as a whole, dependent on varying the total

surface cover fractions.

2. RELATED WORK

The UHI effect describes the phenomena of higher temperatures of both the atmosphere and surfaces in urban areas

compared to their non-urbanized surroundings (Unger, 2004). The phenomenon is typically more intense at night when

the heat absorbed during the day is released into the atmosphere (Oke, 1982a). Furthermore, the effect is influenced

by local weather conditions and is usually larger in the urban core than on the urban periphery.

The UHI is a well documented example of anthropogenic climate modification (Arnfield, 2003). In the past three

decades, the phenomenon has been extensively investigated and quantified in various empirical studies (Oke 1982b;

Balling Jr. & Brazel, 1987; Eliasson & Holmer, 1990; Golden, 2004; Chow & Roth, 2006). Research has also been

dedicated to urban design and vegetation as possible mitigation strategies for UHIs, especially the investigation of

thermal benefits of urban parks (Sponken-Smith & Oke, 1999; Yu & Hien, 2006; Upmanis, Eliasson, & Lindqvist,

1998).

To evaluate spatio-temporal trends in temperature, Phoenix, AZ, in the arid Southwestern United States is an excellent

location, since the city is characterized by rapid, sprawling, and uncontrolled urbanization (Gammage, 2003). Brazel et

al. (2007) investigated the spatial and temporal variation in June mean minimum temperatures for weather stations in

and around metropolitan Phoenix and observed an overall spatial urban effect in the order of 2 to 4K. Guhathakurta

and Gober (2007) statistically analyzed the impact of the Phoenix UHI on residential water use and demonstrated that

an increase in daily low temperatures by 1°F results in an increasing water demand of 290 gallons per household.

Since the late 80’s, Grimmond and Oke have dedicated their research to modelling the relationship between water and

urban temperature. Their SUES model (Single-source Urban Evapotranspiration-interception Scheme) can be used to

calculate evapotranspiration from urban areas over a wide range of meteorological conditions (Grimmond & Oke,

1991). In 1999, Grimmond and Oke examined empirically the storage heat flux for seven urban areas and evaluated

the performance of their local-scale OHM (Objective Hysteresis Model), which calculates the storage heat flux as a

function of net all-wave radiation and the surface properties of the site (Grimmond & Oke, 1999a). They also

addressed aerodynamic properties of urban areas through analysis of surface form (Grimmond & Oke, 1999b).

Based on their formulation of heat storage and partitioning of heat fluxes from prior work, Grimmond and Oke finally

introduced a local-scale urban meteorological parameterization scheme to calculate heat fluxes in urban areas

(Grimmond & Oke, 2002).

Mitchell, Cleugh, Grimmond, and Xu (2007) were the first to employ energy balance models in a scenario manner.

They used two different evapotranspiration models to demonstrate the impact of a range of water sensitive urban

design strategies on water exchanges and inferred from the energy balance how different urban designs would affect

air temperature. Two years later, Gober et al. (2009) conducted a pilot study in collaboration with the City of Phoenix

Water Resources Department using the LUMPS model (Grimmond & Oke, 2002) to evaluate scenario-based

temperature and evaporation changes for 10 census tracts of the Phoenix urban core. They designed three scenarios

to represent a more compact city, an oasis city, and a desert city. As model input served meteorological hourly data

and detailed surface characteristics that were extracted from satellite images with an object-based classification

approach (Myint, Giri, Wang, Zhu, & Gillette, 2008). The study was presented at the 89th Annual Meeting of the

American Meteorological society (Brazel A. J., et al., 2009) and showed that the LUMPS model is a valuable tool for

exploring different urban design strategies for UHI mitigation.

3. STUDY AREA

The City of Phoenix and its surrounding metropolitan area is located in the northern reaches of the Sonoran Dessert in

the state of Arizona, which is located in the southwestern part of the United States of America. Considering the mean

annual temperature of 65°F degrees, the Sonoran Desert is one of the warmest places in the United States (Ives,

1949).

The Phoenix metropolitan area is one of the largest and fastest growing urbanized areas in the United States. The city

and its surroundings have experienced significant changes in terms of population growth over the past decades.

Today, more than 4 million people live in the Phoenix metropolitan area. In 1970, the Greater Phoenix area had only

971,000 residents. It is projected that the region will continue to grow and have more than 7 million residents by the

year 2030 (Arizona Department of Economic Security, 2008). The increasing demand for new urban developments

and the favored settlement pattern of single housing leads to a rapid spatial growth of the urban area. So far, the

expansion of the Phoenix metropolitan area has been excursive, successive, and widely strategically uncontrolled

(Gammage, 2003). As a result, various problems emerged regarding urban developments which are characteristic for

the issues other metropolitan areas in the United States are confronted with.

The Phoenix economy is heavily dependent on land development and real estate construction (Gammage, 2003;

Gober, 2005). This trend has led to rapid urbanization, low-density development patterns and urban sprawl, entailing a

large amount of soil sealing which is considered one of the main reasons for the UHI. Consequently, an extensive

Figure 1: Study area: 52 census tracts located in and around the urban core of the City of Phoenix, AZ, USA

sealed urbanized area was created within one of the hottest regions in the United States, limiting the city’s ability to

cool off at night and to establish a healthy environment. The UHI dramatically alters comfort levels, increases the

consumption of energy and water, and decreases the quality of life. UHI mitigation strategies include planting more

trees and using irrigated vegetation to abate heat rather than retain it, but there seems to be a limit beyond which

adding plants has little positive cooling effect. This project aims at analyzing the tradeoffs between vegetating urban

regions to reduce nighttime and daytime temperatures and increasing water demand for irrigating the plants. The

objective is to optimize temperature reduction and water use by designing a water- and energy-sensitive urban

environment.

4. METHODOLOGY AND DATA

We use a simple energy budget model to calculate the urban energy and water balance for 52 census tracts in

Phoenix, AZ, for a simulation of three different urban land cover scenarios to mitigate the UHI. The study sites are

located in and around the urban core and fit the local-scale model’s requirements for horizontal input areas of

approximately 102-104m (compare Figure 1). While some of the census tracts are characterized by industrial

developments with large buildings, high percentage of sealed surfaces, and few plants, the other neighbourhoods are

primarily residential. They either have a high proportion of irrigated mesic landscaping, or consist of predominantly dry

xeric landscaping. Altogether, the selected census tracts encompass a representative cross section of the south-

western desert city land coverage.

4.1 The LUMPS model

To simulate the summertime atmospheric heating/cooling and water use in Phoenix at the local scale we employ the

LUMPS model after Grimmond and Oke (2002). LUMPS stands for “Local-scale Urban Meteorological

Parameterization Scheme”. The model is composed of a set of linked equations for calculating the basic energy

budget of a local area, more precisely, the storage heat flux ΔQS, turbulent sensible heat flux QH, and latent heat flux

QE, given the net all-wave radiation Q*: Q* = QH + QE + ΔQS (surface energy balance)

The net all-wave radiation Q* sets the energetic bounds for QH, QE and ΔQS in this surface energy balance and thus

drives the scheme. In the absence of Q* observations, this term can be obtained from parameterization using

measured or modeled solar radiation (Grimmond & Oke, 2002). The model assumes that heat fluxes can be calculated

using the net all-wave radiation, basic land cover information on vegetation, buildings, and impervious surfaces, as

well as morphometric variations (roughness of the canopy layer) and standard weather observations such as air

temperature, humidity, wind speed, and pressure. Although this model has relatively limited data requirements, it is

sophisticated enough to predict the spatial and temporal variability of heat fluxes known to occur within and between

urban areas (Grimmond & Oke, 2002).

Figure 2 illustrates the layers relevant for local-scale urban climate modeling and the box modeled by LUMPS. Here,

local-scale refers to a box of (102-104) x (102-104) m2. The characteristic neighborhood response is simulated at the

top of the box -- the measurement height above the city in the inertial sublayer above the urban canopy and its

roughness sublayer. Within the inertial sublayer, the spatial variability of fluxes is smaller averaged over time than in

the roughness sublayer. While fluxes are chaotic in the canopy layer and the roughness sublayer, they finally become

invariant in the inertial sublayer where the top of the LUMPS box is located. At this height and scale, the microscale

variability of atmospheric effects generated by individual buildings and other surfaces is expected to be integrated into

a characteristic neighborhood response (Grimmond & Oke, 2002).

Figure 2: Definition of climate layers relative to the box modeled by LUMPS (after Grimmond & Oke, 2002)

As input, the LUMPS model requires two sets of parameters: (a) a text file containing hourly meteorological data on

solar radiation, wind, temperature, humidity, and pressure, and (b) detailed land cover fractions for each study site. We

run the model by including a meteorological file from the high-quality National Weather Service data at Sky Harbor

Airport for June, 2007. The airport is located near the south-east corner of the study area. Detailed land-surface

fractions for the 52 census tracts were identified from Quickbird imagery at 2.5m spatial resolution, acquired on May

29, 2007, using the object-based classification approach presented in (Gober, et al., 2009). The study area comprises

diverse land use and land cover classes, varying from urban developments (commercial, industrial and residential) to

undeveloped regions in the form of grassland, soil, open water, and desert. Following the approach of (Gober, et al.,

2009), we establish six land cover classes for recognition in the Quickbird images: buildings, impervious surfaces,

unmanaged soil, trees and shrubs, grass, and water bodies.

The LUMPS simulation output files contain the hourly energy budget components defined in section 3.1, namely the

turbulent sensible heat flux QH, the latent heat flux QE, the heat storage ΔQS, and the all-wave net radiation Q*, all

variables in units of Wm-2. Furthermore, the file specifies the evaporation for the investigated area in units kgm-2 per

hour.

Grimmond and Oke (2002) provide technical model details and a profound evaluation of their LUMPS model. They

calibrate the model using extensive observations in various urban areas, including Mexico City, Miami, Tuscon, Los

Angeles, Sacramento, Vancouver, and Chicago. Furthermore, Gober et al (2009) show that LUMPS is capable of

simulating actual evaporation and temperature conditions in Phoenix, AZ. They use water meter records from the City

of Phoenix, adjusted for outdoor water use, and remotely sensed thermal images to validate LUMPS estimates. The

model results strongly support that LUMPS is a suitable tool to quantify the correlation between nighttime cooling and

outdoor water use.

4.2 Land cover fraction scenarios

We designed three different scenarios to determine the interplay of water use and diurnal variations in summer of

atmospheric heating and cooling processes. The scenarios represent a realistic set of possible future development

patterns in the Phoenix Metropolitan area. For each scenario, we gradually increased specific land cover fractions to a

maximum and calculated the corresponding LUMPS energy budget values to evaluate the resulting temperature and

evaporation changes. Table 1 lists the census tracts illustrated in Figure 1 and summarizes the base land cover

fractions determined from the Quickbird imagery for the 52 neighborhoods in the City of Phoenix. Additionally, the

table specifies which census tracts are used in each scenario.

The first scenario (scenario X) assumes xeric landscaping across the selected census tracts, occasioned by a gain in

soil equivalent to a loss in grass from present day conditions shown in Table 1. The fractions for buildings, impervious

surfaces, water, and trees and shrubs remain constant. Scenario X is only calculated for the 32 census tracts where

grass coverage is equal to or more than 20%, since it is not reasonable to take grass out of already dry sites.

The second scenario (scenario B) simulates infill and Brownfield development or densification, respectively. While the

land cover fractions for trees, grass, and water are unchanged, soil is gradually replaced with one third impervious

increase and two thirds building increase. Again, we choose fraction thresholds of less than 20% buildings and a total

of more than 35% buildings plus unmanaged soil to select sprawled and developable sites for this scenario.

Table 2: Land cover fractions, size, and calculated scenarios for 52 census tracts in Phoenix, AZ, USA

Census Tract Land Cover Fractions Scenarios

No. Total m2 Buildings Unmanaged Soil Grass Impervious

Surfaces Trees

& Shrubs

Pools, Lakes, Ponds & Canals

X B G

1060 3,096,593 0.19885 0.14227 0.32072 0.20710 0.12850 0.00256 x1061 3,057,909 0.19825 0.08927 0.36137 0.14728 0.19689 0.00693 x1062 3,107,906 0.16793 0.08581 0.38052 0.10215 0.25403 0.00955 x1063 3,190,683 0.20073 0.16596 0.28014 0.18360 0.15582 0.01376 x x x1064 3,118,435 0.17750 0.25375 0.24179 0.18359 0.12187 0.02150 x x x1065 3,142,045 0.21762 0.12531 0.33266 0.16317 0.15673 0.00451 x1066 3,116,477 0.19287 0.07176 0.36090 0.12573 0.24100 0.00775 x1067 3,078,732 0.20095 0.10093 0.35581 0.17761 0.15918 0.00552 x1068 3,081,151 0.21661 0.15147 0.27609 0.24747 0.10426 0.00411 x x x1073 3,089,837 0.20920 0.18060 0.25289 0.26825 0.08742 0.00164 x x x1074 3,099,531 0.22709 0.14965 0.25097 0.24562 0.12367 0.00300 x x1075 3,127,317 0.18342 0.08288 0.31059 0.19907 0.21907 0.00497 x1076 3,133,020 0.22268 0.10343 0.25641 0.23922 0.17479 0.00347 x1077 3,138,676 0.17026 0.11887 0.32019 0.19090 0.19514 0.00463 x1085 3,095,948 0.25592 0.12332 0.19959 0.29702 0.12146 0.00269

1086-01 1,002,102 0.21563 0.21575 0.19123 0.26757 0.09540 0.01442 x x1086-02 2,130,261 0.21009 0.15166 0.21652 0.30250 0.11201 0.00723 x x x

1087 931,524 0.13956 0.17756 0.36911 0.19797 0.09512 0.02069 x x1088-01 986,227 0.22841 0.18728 0.25089 0.21882 0.10782 0.00678 x x x1088-02 1,242,438 0.20140 0.16855 0.26778 0.22789 0.12254 0.01185 x x x1089-01 1,254,626 0.19246 0.12605 0.34226 0.18199 0.14741 0.00983 x1089-02 1,799,482 0.23304 0.15022 0.28450 0.21074 0.11243 0.00907 x x x

1090 3,068,531 0.23747 0.16632 0.17906 0.32716 0.08472 0.00527 x x1103 3,039,967 0.21769 0.17793 0.25053 0.24764 0.10050 0.00571 x x x1104 2,985,800 0.21470 0.13163 0.30947 0.22481 0.11710 0.00228 x1105 3,259,469 0.26732 0.13986 0.14854 0.33709 0.10540 0.00179 1106 3,114,207 0.15328 0.12162 0.35485 0.16895 0.19493 0.00637 x

1107-01 1,008,403 0.19315 0.16179 0.22914 0.28554 0.11757 0.01282 x x x1107-02 2,117,658 0.19612 0.16447 0.28012 0.23133 0.12110 0.00686 x x x1116-01 1,546,065 0.17998 0.18210 0.26985 0.26072 0.10301 0.00433 x x x1116-02 1,575,878 0.22759 0.21252 0.23200 0.23635 0.09099 0.00055 x x x

1117 3,114,173 0.22796 0.17140 0.31548 0.16941 0.11464 0.00111 x x x1118 3,169,227 0.20235 0.11747 0.28179 0.22640 0.16989 0.00210 x1119 3,082,971 0.12440 0.07513 0.44498 0.16981 0.17342 0.01226 x1120 3,110,538 0.23202 0.17967 0.10876 0.42609 0.05214 0.00132 x x1128 3,137,910 0.13263 0.23083 0.17659 0.34856 0.11138 0.00000 x x1129 3,116,010 0.22127 0.24614 0.17209 0.28431 0.07537 0.00081 x x1130 1,533,473 0.18605 0.18192 0.21477 0.30188 0.11286 0.00252 x x x1131 1,560,516 0.22361 0.26922 0.09824 0.33995 0.06831 0.00067 x x

1132-01 778,867 0.24506 0.33320 0.20149 0.17227 0.04783 0.00015 x x1132-02 806,734 0.21061 0.34792 0.17741 0.20978 0.05409 0.00018 x x1132-03 1,527,777 0.18779 0.25212 0.16029 0.31392 0.08553 0.00036 x x

1133 3,097,273 0.15937 0.30513 0.15509 0.31857 0.06168 0.00017 x x1134 798,676 0.23494 0.26637 0.17697 0.27031 0.05087 0.00054 x x1139 3,201,725 0.18110 0.33749 0.07000 0.37308 0.03823 0.00010 x x1140 3,125,244 0.23885 0.21828 0.06262 0.43668 0.04308 0.00049 x x1141 1,573,200 0.36939 0.14282 0.05305 0.39266 0.04193 0.00014 1142 1,523,917 0.20495 0.30387 0.07665 0.38966 0.02436 0.00051 x x

1143-01 1,584,012 0.23246 0.23081 0.11911 0.35168 0.06590 0.00004 x x1143-02 1,572,958 0.21225 0.36251 0.08165 0.32412 0.01928 0.00020 x x1144-01 1,641,583 0.29844 0.26357 0.09124 0.32539 0.02130 0.00007 x1144-02 1,452,626 0.21156 0.29076 0.10132 0.36914 0.02719 0.00004 x x

Third, we gradually reduce land covered in barren soil and at the same time increase the percentage of grass and

trees in a ratio of 2:1 towards a green city (scenario G). While reducing soil to zero and transforming it into green

spaces we apply twice the increase for grass versus trees. Scenario G is calculated for 34 census tracts in Phoenix

with a soil percentage of more than 15% to ensure sufficient planting area.

5. RESULTS

The output from LUMPS for daily ET (evapotranspiration) in the form of heat to evaporate water was converted to

mass (Kgm-3 and ccf/QuickBird pixel – ccf is hundreds of cubic feet of water, a value commonly used by the City Water

Department). This was calculated for the current base case and for the scenarios defined in the last section, by

changing the land fractions according to the objectives outlined. We use the same weather file for the scenarios that is

required to generate results for the LUMPS model. The results of the scenarios are presented in Tables 2-4. To

achieve an estimate of the resulting heating and cooling effects in the boundary layer immediately above the

residential areas, we used an expression after Mitchell, Cleugh, Grimmond, and Xu (2007) that calculates the rate of

temperature change per hour from knowledge of the sensible heating during the day and cooling at night, in addition to

the boundary depth. We estimated the typical boundary layer height from results of Grossman-Clarke, Zehnder,

Stefanov, Liu, and Zoldak (2005) and for nighttime by iterating the height until the cooling rate magnitudes calculated

were in the range experienced by cooling rates recorded from nearby hourly recording weather networks. Once

calculations were achieved for ET and for the heating and cooling rates (H and C, respectively, expressed in °C/hr),

we derive a ratio of H and C to ET, so as to measure the amount of heating to consider per water loss, and the amount

of cooling benefit at night per amount of water loss. As shown in all tables, these ratios are expressed as a positive

value for daytime and negative value (cooling rate dictates this) at night. Landscapes with very little water in the base

case experienced higher daytime heating and excess heat storage at night.

To judge the decisions of the three scenarios that could be commonly thought of as mitigation or planning measures

(xeriscaping, densification, and greening) and their possible consequences on impacting the current H/ET and C/ET

ratios, we take the base case ratios and subtract the ratios derived in the scenarios from this base case. In so doing,

we arrive at values expressed in the final columns on the far right side of each scenario table. It should be emphasized

that for daytime, if resultant values are positive this means that there is more heating in the base case per water loss

than the H/ET values in the scenario case. For nighttime (since the cooling rate is expressed as a negative number), if

the difference between the base minus the scenario is positive, this would mean it is the case that the scenario -C/ET

values exceed the negative rates of C/ET of the base case, i.e., the base minus scenario would end up as a positive

number. So as regards nighttime, positive numbers suggest a net change to beneficial cooling per water loss

experienced compared to the base case.

SCENARIO X (XERISCAPING)

Census Tract No.

change in land cover fraction monthly evaporation

per hr heating

rate

per hr cooling

rate

∆ = (base – scenario)

daytime heating impact

nighttime cooling impact

soil grass [kgm-2] [ccf/pixel] [C°] [C°] ∆ ∆

1060 +32.1% -32.1% 29.7430 0.0605 1.0649 -1.4795 -7.6488 8.6353 1061 +36.1% -36.1% 34.5645 0.0703 1.0942 -1.9613 -6.9470 10.6889 1062 +38.1% -38.1% 38.6170 0.0786 1.1072 -2.2392 -6.2847 11.1199 1063 +28.0% -28.0% 32.1049 0.0653 1.0713 -1.6695 -6.3305 8.1649 1064 +24.2% -24.2% 30.5325 0.0621 1.0624 -1.4309 -6.0923 6.5369 1065 +33.3% -33.3% 31.9541 0.0650 1.0926 -1.7949 -7.3228 10.1698 1066 +36.1% -36.1% 37.4669 0.0762 1.1018 -2.1941 -6.2791 10.9011 1067 +35.6% -35.6% 31.9586 0.0650 1.0799 -1.7131 -7.5284 10.0148 1068 +27.6% -27.6% 28.0661 0.0571 1.0449 -1.2957 -7.3455 7.1386 1073 +25.3% -25.3% 26.8088 0.0545 1.0326 -1.1297 -7.2852 6.0055 1074 +25.1% -25.1% 29.1145 0.0592 1.0491 -1.4287 -6.5581 7.1406 1075 +31.1% -31.1% 35.0776 0.0714 1.0621 -1.8696 -5.9305 8.8028 1076 +25.6% -25.6% 32.0511 0.0652 1.0507 -1.6741 -5.8043 7.6164 1077 +32.0% -32.0% 33.7744 0.0687 1.0646 -1.7477 -6.4010 8.7716

1086-02 +21.7% -21.7% 28.0566 0.0571 1.0097 -1.1472 -5.9328 5.0065 1087 +36.9% -36.9% 28.7923 0.0586 1.0462 -1.1428 -8.5646 7.1113

1088-01 +25.1% -25.1% 28.7027 0.0584 1.0627 -1.4321 -6.8170 7.3842 1088-02 +26.8% -26.8% 29.6411 0.0603 1.0481 -1.3873 -6.6871 7.0152 1089-01 +34.2% -34.2% 31.4402 0.0640 1.0732 -1.6153 -7.4607 9.2908 1089-02 +28.5% -28.5% 29.1622 0.0593 1.0672 -1.4889 -7.2920 8.2560

1103 +25.0% -25.0% 27.9359 0.0568 1.0443 -1.2778 -6.9213 6.5926 1104 +30.9% -30.9% 28.9216 0.0588 1.0586 -1.4217 -7.7109 8.3458 1106 +35.5% -35.5% 34.0952 0.0694 1.0723 -1.7573 -6.8274 9.3838

1107-01 +22.9% -22.9% 28.7818 0.0585 1.0122 -1.1673 -5.9746 5.1505 1107-02 +28.0% -28.0% 29.2761 0.0596 1.0478 -1.3594 -7.0127 7.1972 1116-01 +27.0% -27.0% 27.8313 0.0566 1.0288 -1.1362 -7.1850 5.9473 1116-02 +23.2% -23.2% 27.2870 0.0555 1.0561 -1.3010 -6.8914 6.6710

1117 +31.5% -31.5% 29.3213 0.0595 1.0948 -1.6160 -8.0013 9.8367 1118 +28.1% -28.1% 31.8341 0.0648 1.0542 -1.6296 -6.2906 7.9997 1119 +44.5% -44.5% 33.1023 0.0673 1.0624 -1.5639 -8.0776 9.8149 1130 21.5% -21.5% 27.8603 0.0567 1.0072 -1.0843 -5.9432 4.6673

1132-01 20.5% -20.5% 25.4904 0.0519 1.0986 -1.3346 -7.2996 7.1075 Table 3: Results for scenario X (xeriscaping, reduction of grass to zero, increase in soil by 20-45%) From surveying these three tables, we observe several results and relationships. Assuming a strategy for xeriscaping,

as assumed in the scenario construct, there are results with differing and dramatic net effects from the base case –

large heating effects during the day but beneficial changes driven primarily by the savings of water for night.

SCENARIO B (DENSIFICATION)

Census Tract No.

change in land cover fraction monthly evaporation

per hr heating

rate

per hr cooling

rate

∆ = (base – scenario) daytime heating impact

nighttime cooling impact

soil buildings imperv. surfaces [kgm-2] [ccf/pixel] [C°] [C°] ∆ ∆

1063 -12.6% +12.6% +4.0% 46.2340 0.0940 0.9618 -1.8847 -0.1525 2.6406 1064 -25.4% +19.4% +6.0% 42.4476 0.0864 0.9741 -1.7683 -0.2683 3.9782 1068 -15.1% +11.6% +3.5% 41.6853 0.0848 0.9433 -1.5327 -0.1672 2.5183 1073 -18.1% +13.6% +4.5% 39.0343 0.0794 0.9405 -1.4393 -0.1952 3.4160 1074 -15.0% +11.5% +3.5% 41.5132 0.0844 0.9557 -1.6211 -0.1620 2.2133

1086-01 -21.6% +16.5% +5.1% 36.9989 0.0753 0.9618 -1.5452 -0.2667 4.5780 1086-02 -15.2% +11.7% +3.5% 38.4456 0.0782 0.9338 -1.4219 -0.1815 3.0877 1088-01 -18.7% +14.2% +4.5% 41.1132 0.0836 0.9691 -1.6730 -0.2024 2.8613 1088-02 -16.9% +12.9% +4.0% 42.8766 0.0872 0.9481 -1.6522 -0.1742 2.9505 1089-02 -15.0% +11.5% +3.5% 43.4234 0.0883 0.9589 -1.7204 -0.1578 2.6340

1090 -16.6% +12.6% +4.0% 34.7076 0.0706 0.9414 -1.3248 -0.2137 3.7426 1103 -17.8% +13.5% +4.3% 40.1858 0.0817 0.9522 -1.5488 -0.1928 3.0524

1107-01 -16.2% +12.2% +4.0% 39.7762 0.0809 0.9297 -1.4393 -0.1752 3.0012 1107-02 -16.4% +12.4% +4.0% 43.1121 0.0877 0.9424 -1.6172 -0.1639 2.8109 1116-01 -18.2% +13.7% +4.5% 40.9131 0.0832 0.9300 -1.4457 -0.1880 3.2490 1116-02 -21.3% +16.3% +5.0% 38.6317 0.0786 0.9732 -1.6555 -0.2495 4.2985

1117 -17.1% +13.1% +4.0% 45.3967 0.0923 0.9716 -1.8575 -0.1672 2.8575 1120 -18.0% +13.5% +4.5% 28.2337 0.0574 0.9113 -0.8728 -0.2733 4.9534 1128 -23.1% +17.6% +5.5% 35.1776 0..0716 0.9135 -1.1916 -0.2921 5.0739 1129 -24.6% +18.6% +6.0% 34.0447 0.0693 0.9687 -1.4767 -0.3234 5.6274 1130 -18.7% +13.7% +4.5% 38.0501 0.0774 0.9313 -1.3951 -0.2034 3.5591 1131 -26.9% +20.4% +6.5% 29.3727 0.0597 0.9672 -1.3240 -0.4206 7.3813

1132-02 -34.8% +26.3% +8.5% 33.8704 0.0689 1.0097 -1.7145 -0.4642 8.0421 1132-03 -25.2% +18.7% +6.5% 33.4876 0.0681 0.9472 -1.3311 -0.3160 5.5596

1133 -30.5% +23.0% +7.5% 31.8049 0.0647 0.9443 -1.2213 -0.4200 7.4132 1134 -26.6% +20.1% +6.5% 33.1142 0.0674 0.9789 -1.4765 -0.3612 6.2942 1139 -33.7% +25.5% +8.2% 25.9280 0.0527 0.9497 -1.0481 -0.5843 10.4777 1140 -21.8% +16.5% +5.3% 25.4574 0.0518 0.9249 -0.8674 -0.3901 6.9173 1142 -30.4% +22.9% +7.5% 25.4618 0.0518 0.9408 -0.9450 -0.5306 9.5269

1143-01 -23.1% +17.6% +5.5% 30.1693 0.0614 0.9545 -1.2532 -0.3514 6.1876 1143-02 -36.3% +27.3% +9.0% 25.9934 0.0529 0.9805 -1.2302 -0.6268 11.1632 1144-02 -29.1% +22.1% +7.0% 26.9994 0.0549 0.9472 -1.0387 -0.4921 8.7240

Table 4: Results for scenario B (densification, reduction of soil to zero, buildings increased 11-26%, increase of impervious surfaces by 4-10%) For the densification of housing scenario, there is more than an order of magnitude smaller heating/water loss during

the day, and surprisingly a beneficial change in the C/ET ratio at night. More simulations would uncover the reason for

this nighttime change, but it is thought the increased building fraction (without impacting green fraction as is the case

in this scenario) causes a bit more ET and less heat storage. The raw energy flux data from LUMPS illustrates this to

be the case. Over further increased building fractions, this may not be the case as one would likely have to reduce the

green space further.

SCENARIO G (GREEN CITY)

Census Tract No.

change in land cover fraction monthly evaporation

per hr heating

rate

per hr cooling

rate

∆ = (base – scenario) daytime heating impact

nighttime cooling impact

soil trees & shrubs grass [kgm-2] [ccf/pixel] [C°] [C°] ∆ ∆

1063 -16.6% +5.5% +11.1% 55.6332 0.1132 0.9062 -1.8288 2.0663 -1.2394 1064 -25.4% +8.4% +17.0% 56.6796 0.1153 0.8890 -1.7383 3.3027 -1.4243 1068 -15.1% +5.0% +10.1% 49.9542 0.1016 0.8944 -1.5268 2.1556 -0.532 1073 -18.1% +6.0% +12.1% 48.8347 0.0994 0.8865 -1.4340 2.7253 -0.2751

1086-01 -21.6% +6.6% +15.0% 48.6344 0.0989 0.8938 -1.5105 3.4775 -0.6855 1086-02 -15.2% +5.0% +10.2% 46.5325 0.0947 0.8871 -1.4055 2.3869 -0.2456

1087 -17.8% +5.8% +12.0% 57.0354 0.1160 0.8497 -1.4153 1.9752 -0.202 1088-01 -18.7% +6.0% +12.7% 51.4761 0.1047 0.9081 -1.6738 2.7125 -1.1585 1088-02 -16.9% +5.5% +11.4% 52.1824 0.1061 0.8935 -1.6127 2.2789 -0.8001 1089-02 -15.0% +5.0% +10.0% 51.7695 0.1053 0.9094 -1.6810 2.0627 -0.8805

1090 -16.6% +5.5% +11.1% 43.4707 0.0884 0.8929 -1.3269 3.0238 -0.0173 1103 -17.8% +5.8% +12.0% 49.9114 0.1015 0.8963 -1.5441 2.6275 -0.6855

1107-01 -16.2% +5.2% +11.0% 48.5017 0.0987 0.8805 -1.4254 2.3908 -0.3403 1107-02 -16.4% +5.4% +11.0% 52.2070 0.1062 0.8903 -1.5886 2.1986 -0.6708 1116-01 -18.2% +6.0% +12.2% 50.8366 0.1034 0.8744 -1.4337 2.5307 -0.2579 1116-02 -21.3% +7.0% +14.3% 50.2708 0.1023 0.9049 -1.6118 3.2854 -1.0061

1117 -17.1% +5.5% +11.6% 55.0790 0.1120 0.9125 -1.7911 2.2095 -1.2705 1120 -18.0% +6.0% +12.0% 37.2499 0.0758 0.8655 -0.9263 4.1707 1.9803 1128 -23.1% +7.5% +15.6% 47.2327 0.0961 0.8465 -1.1952 3.6635 0.8614 1129 -24.6% +8.0% +16.6% 47.2261 0.0961 0.8948 -1.4598 4.3511 -0.5007 1130 -18.2% +6.0% +12.2% 47.8017 0.0972 0.8776 -1.3918 2.803 -0.1525 1131 -26.9% +9.0% +17.9% 43.4111 0.0883 0.8900 -1.3350 5.6878 0.3388

1132-01 -33.3% +11.0% +22.3% 53.9452 0.1097 0.9192 -1.7884 5.5114 -2.3343 1132-02 -34.8% +11.5% +23.3% 53.0258 0.1079 0.8995 -1.6567 5.8515 -1.4832 1132-03 -25.2% +8.2% +17.0% 46.8813 0.0954 0.8760 -1.3487 4.4023 0.1609

1133 -30.5% +10.0% +20.5% 47.8455 0.0973 0.8570 -1.2401 5.3706 1.2773 1134 -26.6% +8.6% +18.0% 47.4077 0.0964 0.8983 -1.4580 4.8568 -0.5057 1139 -33.7% +11.0% +22.7% 43.1411 0.0878 0.8579 -1.1007 7.646 3.1488 1140 -21.8% +7.0% +14.8% 36.3019 0.0738 0.8681 -0.9185 5.7154 2.6048 1142 -30.4% +10.0% +20.4% 40.8364 0.0831 0.8608 -1.0166 7.2716 3.5199

1143-01 -23.1% +7.5% +15.6% 42.1311 0.0857 0.8881 -1.2599 4.8387 0.4678 1143-02 -36.3% +12.0% +24.3% 44.8678 0.0913 0.8795 -1.2727 8.2803 1.8415 1144-01 -26.4% +8.5% +17.9% 40.5528 0.0825 0.9183 -1.3616 6.6427 -0.3034 1144-02 -29.1% +9.5% +19.6% 41.8422 0.0851 0.8671 -1.0813 6.5658 2.5153

Table 5: Results for scenario G (greener city, reduction of soil to zero, grass increase 10-25%, trees & shrubs increase 5-13%)

For the greener city scenario, there is a positive change in H/ET during the day indicating less heating from the base

case per water loss. For night, though, net changes actually indicate the base case was more beneficial except for

several very low wet fraction census tracts which would benefit from this scenario change. These census tracts are in

fact the green-deprived neighborhoods of industrial-commercial areas of the city shown at the south end in Figure 1.

This illustrates that adding massive amounts of water in already mesic type neighborhoods would not benefit cooling

much more, and certainly might actually be a disbenefit in terms of the tradeoff of temperature cooling versus water

used.

scenario nighttime cooling daytime heating

∆ R2 Sigma level ∆ R2 Sigma level

X (xeriscaping) 4.201+25.551WF 0.502 0.000 -7.639+5.093 WF 0.119 0.053 B (densification) 11.044-21.126WF 0.828 0.000 -0.514+0.867WF 0.666 0.000 G (green city) 6.415-12.552WF 0.525 0.000 10.962-13.688WF 0.361 0.000

Table 6: Change in heating/cooling impact from base case to scenario case, WF = total wet fraction (grass, trees and shrubs, and open water)

As a generalization of the tabular results in Tables 2-4, the net changes (base-scenario) in the H/ET and C/ET ratios

were correlated with the scenario wet fractions of each census tract studied. Table 5 lists useful, simple equations that

can be used based on desires to alter planned green fractions and what might be the possible tradeoffs of temperature

changes per amount of water expected to be lost by ET for each of the three scenarios.

6. DISCUSSION AND CONCLUSIONS

We created three different scenarios using the LUMPS model to determine the interplay of water use and diurnal

variations in summer of atmospheric heating and cooling processes for 52 census tracts in Phoenix. The scenarios

account for a realistic set of possible future planning measures in the Phoenix Metropolitan area.

The first scenario (scenario X) simulated xeriscaping by reducing grass to zero and increasing soil by 20-45%

accordingly. The results showed large heating impact during the day, but beneficial cooling per water loss at night.

This positive cooling effect at night is driven primarily by the conservation of water. Outdoor water use is majorly

reduced, but at the cost of nighttime cooling. So as regards xeriscaping, although this scenario is more sustainable in

terms of water use, a city-wide banishment of green spaces is not advisable, especially in areas such as Downtown

Phoenix where people should be encouraged to walk by creating a more pedestrian friendly environment. Increased

temperatures during the day cause discomfort for pedestrians and increase energy use for air conditioning. A study

would have to analyze the tradeoffs between outdoor water use reductions and excess energy demand.

Secondly, we simulated an infill and Brownfield development scenario (scenario D). To densify the census tract

neighborhoods, we reduced soil to zero, increased the building fraction by 11-26%, and added 4-10% impervious

surfaces. For all neighborhoods, a building densification slightly increased the evaporation rate, but caused a

beneficial change in the nighttime cooling efficiency. The main reason for this is the change in heat storage from base

case to scenario. Adding buildings at the expense of soil with no change in wet fraction decreases the heat storage

since less heat is going into the heat storage of soil. This yields a higher nighttime cooling rate, with some excess

heating during the day since the heat storage is also not as great during the day and more heat is going into the air.

Our results demonstrate that the infill development scenario does not increase the UHI effect for residential areas.

However, this might not hold for high rise buildings, since urban canyons increase heat retention.

In the third scenario, we replaced soil by green spaces towards a greener city (scenario G). An increase of grass by

10-25% and trees and shrubs by 5-13% results in more evapotranspiration compared to the base case and less heat

storage for nighttime release. Although the daytime heating impact is positive, meaning temperatures during the day

were more comfortable, the ratio of the amount of cooling at night relative to the evaporation rate shows that the base

case is more beneficial. The water loss involved in nighttime cooling is disproportionally high, except for green-

deprived neighborhoods which might actually benefit from this scenario. Consequently, adding irrigated vegetation to

already green neighborhoods does not seem to be a good tradeoff between cooling effect and water used. A city-wide

implementation of scenario G would lead to a totally unsustainable urban development in terms of water use because

of the climatic conditions. However, increasing the amount of irrigated vegetation could be applied successfully in very

dry neighborhoods as well as in places with outdoor daytime activities such as recreational areas, community places,

and Downtown Phoenix to create a walkable, pedestrian-friendly environment.

From the planning perspective, our scenario-based local-scale model approach assists planners in making better-

informed decision in terms of UHI mitigation strategies. The scenario runs can indicate which urban design is best

applied where – a challenging decision, particularly in desert cities like metropolitan Phoenix where water supply is

crucial for a sustainable future. However, our scenarios can give recommendations for land coverage within

neighborhoods to minimize water use and at the same time maximize environmental goals, but the model cannot

specify the spatial arrangement of land cover fractions. To determine the urban design within neighborhoods more

precisely, a micro-scale model would have to be employed.

Other scenarios we envision besides the ones presented include changing the mean albedo of the land cover fractions

or the albedo of individual surfaces such as roof tops and impervious surfaces, respectively. To refine the densification

scenario, the manipulation of average building heights would be beneficial. As model fine-tunings, we envision more

variables for land cover fractions, e.g., pervious pavements, and leaf indices for trees and plants native to the Sonoran

Desert. Finally, the integration of the model into a geographical information system (GIS) would allow for spatial

analyses of temperature variations in correlation with other relevant geodata.

ACKNOWLEDGEMENTS

This work was supported by the German Science Foundation (DFG, grant number 1131) as part of the International

Graduate School (IRTG) in Kaiserslautern on “Visualization of Large and Unstructured Data Sets. Applications in

Geospatial Planning, Modelling, and Engineering”. Furthermore, this study is based upon research supported by the

National Science Foundation under Grant No. SES-0345945 Decision Center for a Desert City (DCDC). All opinions,

findings, conclusions, and recommendations expressed in this article are those of the authors and do not necessarily

reflect the views of the National science Foundation. Finally, we would like to thank Sue Grimmond for providing the

LUMPS code to calculate the energy budgets.

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