Relationships Between Aerodynamic Roughness 

and Land Use and Land Cover 

in Baltimore, Maryland

GEOLOGICAL SURVEY PROFESSIONAL PAPER 1099-C

Relationships Between Aerodynamic Roughness 

and Land Use and Land Cover 

in Baltimore, Maryland

By FRANCIS W. NICHOLAS and JOHN E. LEWIS, JR.

THE INFLUENCES OF LAND USE AND LAND 

COVER IN CLIMATE ANALYSIS

GEOLOGICAL SURVEY PROFESSIONAL PAPER 1099-C

An example of environmental analysis using 

land use and land cover information

UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON : 1980
UNITED STATES DEPARTMENT OF THE INTERIOR 

CECIL D. ANDRUS, Secretary

GEOLOGICAL SURVEY

H. William Menard, Director

Library of Congress Cataloging in Publication Data

Nicholas, Francis W

Relationships between aerodynamic roughness and land use and land cover in 

Baltimore, Maryland.

(Geological Survey professional paper ; 1099-C)

Bibliography: p.

1. Baltimore—Climate. 2. Land use—Maryland—Baltimore. 3. Aerodynamics.

I. Lewis, John E., 1937———— joint author. II. Title. III. Series: United

States. Geological Survey. Professional paper; 1099-C.

QC981.7.U7N53 551.6 79-600213

For sale by the Superintendent of Documents, U.S. Government Printing Office

Washington, D.C. 20402

Stock Number 024-001-03286-4
CONTENTS

Page

Abstract __.._________________________ Cl

Introduction ___________________________ 1

Parameters in urban climatology ______________ 2

Roughness length _________________________ 3

Surface roughness in urban environments ________ 5

Roughness length estimates from surface descriptions 5

Calculation of surface roughness for Baltimore, Md __ 6

The study area _________________ 6

Sampling scheme __..____________________ 6

Pilot study for acquisition of dimensional data __ 8 

Testing the effects of wind variations on building

silhouettes _______________________ 11

Modifications and processing of dimensional data 14

Basic data description ______________ 15

Descriptions of land use and land cover classification

systems ________________________ 16

USGS Level II system _________________ 17

Baltimore Zoning Ordinance system __________ 19

Baltimore Level III system __________ 20

Page

Spatial distribution of surface roughness in the study 

area _________________________________ C20

Statistical association between land use and land cover 

and aerodynamic roughness ____________________ 21

USGS Level II system _________________ 24 

Baltimore Zoning Ordinance system ______ 25

Baltimore Level III system _____________ 25

The CBD as a unique land use and land cover class 26 

Land use and land cover classes ranked by mean

roughness lengths _______________ 28

Evaluation of aerodynamic roughness by simulation

modeling of the surface climate _______________ 29

Sensitivity testing ________________ 30

Simulation of Baltimore Level III system values. _ 30 

Simulation of CBD values _____________ 31

Applications of land use and land cover information 

in urban climatology _________________ 32

References cited __________________________ 34

ILLUSTRATIONS

FIGURE 1. Diagram showing characteristics of atmospheric flow layers in the lower 

troposphere __________________

2. Graphical determination of roughness 

length ___________________________

3. Baltimore city land use and land cover 

map with study area delimited _____

4. Isometric drawing of the Druid Hill test 

plot __——_________________

5. Sanborn map of the Druid Hill test plot6. Diagram showing effects of wind direction on surface roughness __________

7. Histogram of coefficients of variation for 

data from plot rotation ___________

8. Histogram of 324 surface roughness 

lengths __________________

9. Land use and land cover map of Baltimore, based on USGS Level II classification system ______________________

Page

C3

10

12

13

16

18

FIGURE 10. Histogram and map showing surface 

roughness in the Baltimore study area

11. Diagram showing distribution of surface 

roughness in the Baltimore area _____

12. Diagram showing land use and land cover 

classes ranked by mean values of surface roughness ________________—_—

13. Graph showing surface temperature as a 

function of surface roughness _ — __ —

14. Map showing contours on surface roughness in Baltimore, Md., simulated from 

USGS Level II land use and land cover 

data _____________ — ___ ————— — _

15. Map showing contours on surface roughness in Baltimore, Md., simulated from 

Baltimore Level III land use and land 

cover data _________---_-_—___—_—

Page

C22

27

28

31

33

34

III
IV CONTENTS

TABLES

TABLE 1. Roughness lengths, z0 , determined at 

urban sites ____________________

2. Effects of seasonal changes in vegetation 

on roughness length, by land use and 

land cover type, Sacramento, Calif. _

3. Druid Hill test plot dimensional worksheet ___—_—__________——______

4. Relation between roughness length and 

plot size _——_—___—_——_______

5. Number and percent of sample plots, by 

type of cover and surface roughness 

formula used ___—___—___—______

6. Drag coefficients for different numbers of 

tree rows and tree heights _________

7. Roughness lengths assigned to open areas

8. Procedures for organizing surface description data _______ — ____ — ______

9. Summary of roughness length data ____

10. Land use and land cover in Baltimore according to three different classification 

systems ____—________—___—___ — __

11. Land use and land cover in selected cities

12. USGS Level II land use and land cover

classes applicable to this study _ — ___

12a. USGS Level II classes used in this study

and their roughness lengths —_____—_

13. Baltimore Zoning Ordinance classes according to gross building densities __

14. Baltimore Level III land use and land 

cover classes and their roughness 

lengths system _______—_ — _—_—__

Page 

C5

6

9

10

14

14

15

15

16

17

17

19

19

19

20

TABLE 15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

Page

Statistical techniques used in land use

and land cover analysis ____—_—_— C21

Comparison of the variance ratio for

three land use and land cover systems 24

Differences between the paired means of 

roughness lengths of USGS Level II 

land use and land cover classes ——— 24

Differences between the paired means of 

roughness lengths of Baltimore Zoning 

Ordinance classes ____—__——-——— 25

Differences between the paired means of 

roughness lengths of Baltimore Level 

III land use and land cover classes __ 26

Aerodynamic roughness characteristics

of the Baltimore CBD ______———— 27

Input data (boundary conditions) for the

numerical simulation experiment —— 29

Output data from numerical simulation

model ______—_ —— ——————— — 30

Values of the energy balance components

simulated for Baltimore ———————— 30

Values of the energy balance components

simulated for the Baltimore CBD —— 31

Roughness lengths summarized for USGS

Level II land use and land cover classes 32

Roughness lengths summarized for Baltimore Level III land use and land cover 

classes and the CBD _—_—_—_—___ 32

Some recent climatological models using

roughness length _ — ___ — ___ — __- 33

SYMBOLS USED IN TEXT

Symbol Definition

a Empirical constant of—1.24 

b Empirical constant of 1.19

d Zero-plane displacement, assumed to be near 

roof top level in urban environments

h Average crop height

h* Average height of obstacles

k Von Karman constant, a constant of proportionality approximately equal to 0.42

S Standard deviation

S2 Variance estimate

t Tangential shearing stress

U (z) Steady mean wind flow at height z

V* Shear stress, or friction velocity, denned as the 

square root of t/p

Z Height at which wind speed is measured

Z0 Roughness length

A Lot area

D Test statistic for Q-method

Symbol Definition

E Test statistic for one-way analysis of variance 

(variance ratio)

F Test statistic for one way analysis of variance 

(difference between the class means)

H Sensible heat flux

Hjcw Test statistic for Kruskal-Wallis one-way analysis of variance

L Latent heat flux

TV Number of roughness elements on a lot 

N Average number of observations 

Q "Studentized Range" for a given significance 

level

Rn Sum of net radiation

S Soil heat flux

51 Silhouette area of an average obstacle

52 Specific area, A/N

T Temperature, in degrees Celsius

X Mean value

p Air density
THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

RELATIONSHIPS BETWEEN AERODYNAMIC ROUGHNESS AND 

LAND USE AND LAND COVER IN BALTIMORE, MARYLAND 1

By FRANCIS W. NICHOLAS 2 and JOHN E. LEWIS, JR.

ABSTRACT

Urbanization changes the radiative, thermal, hydrologic, 

and aerodynamic properties of the Earth's surface. Knowledge of these surface characteristics, therefore, is essential 

to urban climate analysis. Aerodynamic or surface roughness of urban areas is not well documented, however, because of practical constraints in measuring the wind profile 

in the presence of large buildings. Using an empirical method 

designed by Lettau, and an analysis of variance of surface 

roughness values calculated for 324 samples averaging 0.8 

hectare (ha) of land use and land cover sample in Baltimore, Md., a strong statistical relation was found between 

aerodynamic roughness and urban land use and land cover 

types. Assessment of three land use and land cover systems 

indicates that some of these types have significantly different 

surface roughness characteristics. The tests further indicate 

that statistically significant differences exist in estimated 

surface roughness values when categories (classes) from different land use and land cover classification systems are 

used as surrogates. A Level III extension of the U.S. Geological Survey Level II land use and land cover classification 

system provided the most reliable results. An evaluation of 

the physical association between the aerodynamic properties 

of land use and land cover and the surface climate by numerical simulation of the surface energy balance indicates 

that changes in surface roughness within the range of values 

typical of the Level III categories induce important changes 

in the surface climate.

INTRODUCTION

The climatic changes accompanying urbanization 

are recognized as responses to new aerodynamic, 

thermal, radiative, and hydrologic properties of the 

local surface. The urban climate is therefore a 

product of surface-induced changes to the components of the mass and energy balance, especially

1 The research reported herein was funded in part by the National 

Aeronautics and Space Administration and U.S. Geological Survey as one 

phase of the Central Atlantic Regional Ecological Test Site (CARETS) 

Project.

2 Francis W. Nicholas, deceased. After serving with the U.S. Geological 

Survey, he joined the faculty of Mankato State University in Minnesota.

3 John E. Lewis is now with McGill University, Montreal, Quebec.

the soil, air, and latent heat transfers. Current research in urban climatology attempts to improve on 

solutions to the energy balance problem. In the application of numerical simulation modeling to the 

surface energy balance, the accurate qualification 

of crucial urban surface parameters such as albedo, 

thermal capacity and conductivity, aerodynamic 

(surface) roughness, and evaporation remains 

problematic.

This paper evaluates the areal quantification of 

surface roughness, the parameter important for its 

role in the turbulent transfer of heat, water, and 

atmospheric pollutants. The observational data 

needed for specifying and quantifying urban surface 

parameters is scarce and costly. Moreover, it is difficult to represent large, complex areas by necessarily limited sampling. In this study the areal 

quantification is achieved by a statistical characterization of building geometry in land use and land 

cover regions in Baltimore, Md. Roughness values 

are calculated from 324 microsamples averaging 

0.8 ha by the use of building geometry and density 

description fitted to an empirical formula. Land use 

and land cover information thus becomes a surrogate or function of building density and geometry 

from which the surface roughness values are subsequently determined.

A specific methodology for acquiring the roughness data includes a capability for measuring roughness changes induced by varying wind directions on 

typical building geometries. Three land use and land 

cover classification systems are assessed for their 

ability to differentiate values of surface roughness. 

In addition, surface roughness is evaluated by numerical simulation modeling for its influence on the 

surface energy balance.

Cl
C2 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

PARAMETERS IN URBAN CLIMATOLOGY

With urbanization significant climatic changes 

occur. Urban structures, impervious surface materials, and emissions of heat and aerosols create 

changes to mass and energy balances at the surface. 

Aerosols change the net radiation balance, reduce 

visibility, create health hazards, and alter precipitation processes. The large thermal admittance from 

typical urban surface materials adds to the contribution of sensible heat in the urban atmosphere by 

such activities as heating, transportation, and industry. Urban surface materials also inhibit infiltration of precipitation while reducing the evapotranspiration opportunity by rapidly channeling runoff 

into storm sewers. Taller and more densely constructed buildings reduce the albedo, slow the mean 

wind, and channel low-level winds. Buildings also 

induce turbulence by increasing the frictional drag. 

Turbulence is the mechanism that transports mass, 

momentum, and energy through the lowest parts of 

the atmosphere. This concern with surface influences has, therefore, made urban climatology a distinctive part of microclimatology.

Problems in urban climatology, because of the 

areal extent of the city, often involve the use of 

space and time scales beyond the traditional limits 

of microclimatology. Vertical dimensions of buildings sometimes exceed the lower 100 meters (m) of 

the atmosphere defined by Munn (1966) as the surface boundary layer. The traditional space scale of 

1 square kilometer (km2 ) or less used to define the 

microscale regime is obviously exceeded in every 

metropolitan area. Thus, the investigation of some 

urban climatology problems crosses into the mesoscale realm, although individual sites may retain a 

microscale definition. Each city is a mosaic of microclimates and the scope of any investigation is set by 

the scale of its total problem.

Recognition of the parameters of the physical environment is fundamental to understanding the 

processes of the urban climate. Like any local climate, urban climate is understood as a response to 

differing surface exchanges of mass, momentum, 

and energy.

The concept of climatonomy introduced by Lettau 

demonstrates the usefulness of parameterizing the 

physical environment (Lettau and Lettau, 1969; 

Lettau and Lettau, 1974). Climatonomy, sometimes 

called synthetic climatology, concerns the functional 

relationship between solar, meteorological, and geographical variables. These relationships, called 

process-response functions, are used to determine 

the magnitude of response to a given set of input

conditions. Climatonomical parameterization therefore, provides the approach needed to understanding the urban atmosphere in order to assess the implications of surface modification. This assessment 

is essentially the objective of simulation modeling.

Interaction between the surface and its overlying 

atmosphere involves the partitioning of available 

energy by competing processes. The replacement of 

soil and vegetation with impervious materials during urban growth reduces the latent heat flux into 

the atmosphere by lowering evapotranspiration. 

During the day when insolation creates a large vertical temperature gradient across the earth-atmosphere interface, urban surface materials possessing 

higher heat capacities and conductivities increase 

the molecular heat flux downward. During the night 

when insolation is absent the higher thermal storage capacity of these urban surface materials contributes to the nocturnal heating of the overlying 

air. The direction of the sensible heat flux is then 

toward the surface.

Aerodynamic roughness, induced by the complex 

three-dimensional surface geometry, affects the degree of turbulent transfer of latent and sensible 

heat into the overlying atmosphere. Furthermore, 

the molecular heat flux into the subsurface is 

coupled to the effects of more latent and sensible 

heat transfer into the atmosphere. The surface 

climate is therefore the result of an interacting set 

of process-response functions.

Simplified numerical simulation models of the 

surface climate such as those by Myrup (1969), 

Outcalt (1972a), and Pandolfo and others (1971) 

provide research tools for evaluating the energy 

balance response to hypothetical and actual surface 

changes. For energy balance simulation, Outcalt 

(1972a) identifies three groups of variables that 

create the climatic environment: temporal, meteorological, and geographic. Temporal data determine 

the solar function for diurnal and seasonal energy 

regimes. The meteorological data specify atmospheric conditions that may enhance or reduce the 

surface energy gradients. Geographic variables 

specify the physical parameters of the surface 

environment.

Improvement of solutions to the surface energy 

balance remains problematic because many of the 

geographic, or surface, parameters such as soil heat 

diffusivity, albedo, evaporation area, and surface 

roughness are only crudely known. Careful point 

sampling may be accurate at the microscale while 

only poorly representing the region (Myrup and 

Morgan, 1972). These long-standing problems have
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C3

been described in climatological research reviews 

during the past century (Landsberg, 1957).

Recent advances in remote sensing technology, 

however, suggest a powerful tool that may overcome the classic problem of describing and quantifying regional models. Myrup (1969) states "information on the physical nature of cities, such as the 

average thermal properties or total green area and 

roughness length of cities is hard to come by. It 

would seem that the gathering of such information 

would be an excellent objective for remote sensing 

technology . . . ." In response to this problem, research objectives increasingly list land use and land 

cover description by remote sensing as a means of 

quantifying the spatial distribution of surface properties. "One objective is to demonstrate the relationship between land use and microclimate, as part of 

a GAP/CARETS program effort to develop land 

use information systems, with implications for the 

determination of the environmental impact of land 

use changes . . ." (Alexander and Lewis, 1972). In 

discussing the urban biosphere and the importance 

of surface structure to energy exchanges in the surface climate, Landsberg (1973) states that "the 

many micrometeorological surveys of cities that 

have been published over the last four decades leave 

no doubt that land use is at the root of the modifications which have biological effects. . . ." The present 

study is principally concerned with surface roughness and its areal quantification based on land use 

and land cover information derived from three different classification systems.

ROUGHNESS LENGTH

Roughness length is defined as the height above 

the surface at which the horizontal component of 

the wind speed approaches zero, measured logarithmically downward from the gradient wind level 

where the free flowing winds are an energy source 

free of surface influences. Roughness length is thus 

some fraction of the thickness of the obstructed 

surface boundary layer in the lower troposphere

(fig. 1).

Below the gradient wind level in the lower troposphere is the planetary boundary layer or region 

where the atmospheric flow is directly influenced by 

the nature of the surface. The outer part of the 

boundary layer, known as the Ekman spiral layer, 

is characterized by winds that change in direction 

and speed as height above the surface decreases in 

reaction to increasing shear stress and increasing 

frictional drag. Energy transmitted downward 

through the spiral layer interacts directly with the 

underlying terrain, and the momentum flux toward 

the surface varies with the roughness of the surface.

Roughness length is calculated on the basis of 

boundary layer theory. The vertical distribution of 

wind speed is a function of both surface roughness 

and the stability of the atmosphere. The following 

empirically determined equations describe a logarithmic wind profile close to the surface in a neutrally 

stratified atmosphere (Slade, 1968). The gradient 

of the wind profile is

du(z) 

dz

v if 

kz

(1)

Height (meters)

1000-2000

30-300

Zero-plane displacement 

(Average roof top level)

Surface

GRADIENT WIND LEVEL 

Momentum source—flow decoupled from surface

OUTER BOUNDARY (EKMAN SPIRAL) LAYER

Interaction according to the height

above the surface

FREE SURFACE BOUNDARY LAYER 

Interaction determined by surface roughness

OBSTRUCTED SURFACE BOUNDARY LAYER

Winds channeled by roughness elements

(buildings) into open areas (streets)

FIGURE 1.—Characteristics of atmospheric flow layers in the lower troposphere.
C4 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

The integrated wind profile is

i? * z

u(z) =—In—. 

k z0

(2)

The integrated wind profile modified to allow for the 

displacement by roughness elements is

* ~

-/ x ^ u(z) = — In

k z0

(3)

where 

u(z)

v*

is the steady mean wind flow at height z ; 

is the shear stress, or friction velocity defined as the square root of t/p, where t is 

the tangential shearing stress, and P is 

the air density. The shear stress has the 

dimensions of velocity-squared, and is 

considered constant in the lowest layers 

of the atmosphere;

k is the von Karman constant, a constant of 

proportionality found by experimentation 

to be approximately 0.42;

z0 is the roughness length;

d is the zero-plane displacement and is assumed to be near roof top level in urban 

environments.

According to the wind profile equations described 

above, the wind speed in the surface boundary layer 

decreases with a logarithmic decrease in height. The 

level at which the horizontal wind speed approaches 

zero, and the rate at which the wind speed decreases with height, are functions of the roughness 

of the surface. The rate of decrease of wind speed 

is important in diffusion applications because the

value of the exchange coefficient depends on the rate 

of change of the wind and not on the speed itself.

Generally, measurements of the speed of the horizontal winds at three or more heights within the unobstructed surface boundary layer are extrapolated 

to yield the roughness length. Figure 2 illustrates, 

however, that the roughness length, zot can be calculated from the y- intercept on a semilogarithmic 

plot of height against mean wind speed when wind 

speed has been measured at only two heights.

The discussion of the logarithmic wind profile has 

thus far assumed an environment with a neutral 

lapse rate. Calculation of the wind profile is least 

complicated for these conditions because turbulence 

is basically mechanical in origin. Turbulence is also 

generated or suppressed by thermal effects. Characterization of the wind profile under neutral conditions is, however, often unrealistic during certain 

times of day. Buoyancy during unstable lapse conditions will greatly enhance turbulence, whereas the 

converse occurs during isothermal or inversion conditions. Over rural areas, neutral conditions may 

occur only twice daily during the transition between 

nighttime stability and daytime superadiabatic lapse 

conditions. A neutral lapse rate, however, may be 

prevalent over an urban area throughout the night 

because the urban heat island tends to prevent nocturnal surface inversions. Using a model of the 

urban heat island, Oke and East (1971) showed that 

the combined effects of a warm and rough surface 

contribute to an adiabatic or neutral mixing layer 

over the city during the night.

LU

o

1000

100

10

ROUGHNESS LENGTH, z0 <

0.1

1 23456789 10 11

MEAN WIND SPEED, IN METERS PER SECOND 

FIGURE 2.—Graphical determination of roughness length. In this example, the roughness length, z0 , is 0.9 cm.
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C5

SURFACE ROUGHNESS IN 

URBAN ENVIRONMENTS

The modest progress in estimating surface roughness in agricultural climatology has not been paralleled in urban applications. The relationship between surface roughness and the wind profile has 

been of interest in crop research as a means of predicting the turbulent transport of properties such as 

heat, moisture, and momentum in the surface boundary layer. In addition, the relative ease of taking 

field measurements of the wind profile over crops 

and the homogeneity of crop heights and spacing 

have facilitated the development of regression equations to predict the surface roughness of particular 

crops (Plate, 1971).

Precise measurements of the wind profile over 

urban structures are made difficult by instrumentation problems. The most serious difficulty is in siting 

an array of anemometers at proper heights. The 

lowest instrument must be placed above the highest 

surface obstacle within the surface boundary layer 

(Lettau, 1967). The expense and impracticality of 

installing high towers prohibits measurement of the 

wind profile in complex built-up urban areas. Because of these difficulties, urban wind profile measurements are scarce.

Where wind profiles have been determined at 

urban sites, measurements were usually possible 

because a high tower capable of supporting instruments existed by chance at a level above the highest 

buildings. Table 1 lists the relatively small number 

of cases cited in the literature where winds have 

been measured at urban or suburban sites. In these 

case studies, building heights never exceeded three 

or four stories, and many were lower. The spatial 

variation of urban surface roughness in cities with 

high-rises and skyscrapers has not been well documented because of these instrumentation problems.

ROUGHNESS LENGTH ESTIMATES FROM 

SURFACE DESCRIPTIONS

The physical and economic restrictions to making 

wind profile measurements above urban structures 

have encouraged experiments in estimating surface 

roughness by indirect methods. Progress in wind 

tunnel modeling of urban aerodynamic characteristics remains far behind that made for more homogeneous surfaces such as those presented by forests 

and uniformly spaced crops. The difficulties in wind 

tunnel modeling are caused by problems in preserving physical similarity at small scales.

Progress in estimating the surface roughness of 

spaced crops by the use of empirically determined 

regression equations (Tanner and Pelton, 1960; 

Kung, 1961) has stimulated investigation of the 

relation between aerodynamic roughness and the 

geometry of the surface elements. Several investigators have determined relationships between the densities and shapes of surface obstacles, and an average surface roughness (Kutzback, 1961; Stearns 

and Lettau, 1963). Bushel baskets and evergreen 

trees were used in one series of studies as control 

roughness elements on the smooth ice of Lake Mendota, Madison, Wis. Thus, measurements of the 

wind profile for various configurations of the roughness elements provided data for the design of a 

generalized predictive model that can be used to 

estimate the average surface roughness of a field 

from the characteristics of its roughness elements 

(Lettau, 1967, 1969, 1970). Use of the model is not 

restricted to homogeneous surfaces, as are regression models, because the model allows for density 

differences in fields of roughness obstacles. This 

allowance makes the "Lettau" model especially applicable to the nonhomogeneous building distribution of urban areas.

TABLE 1.—Roughness lengths, in centimeters, determined at urban sites

City

Minneapolis-St. Paul, Minn. 

London, Ontario 

Philadelphia, Pa. (suburbs) 

Tokyo, Japan 

Kokubunji, Japan 

Liverpool, England 

Cities in Germany 

Kiev, U.S.S.R. 

Columbia, Md.

Structure

150-m television tower 

60-m telephone tower 

300-m television tower 

Mast in city 

Mast in outskirts of city 

Mast in city center 

Unknown 

Unknown 

Mast in suburbs

*„

200 

230 

22-310 ' 

165 

48 

123 

50-200 

450 

107

Reference

Deland and Binowski (1966) 

Davenport (1967) 

Slade (1969) 

Yamamoto and Shimanuki (1970) 

Shiotani (1970) 

Jones and others (1970) 

Myrup and Morgan (1972) 

Myrup and Morgan (1972) 

Maisel (1971)

1 Values varied with wind azimuth.
C6 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

The simplified relation between roughness element size and density is expressed in the 1969 

Lettau formula,

Si

z0 = 0.5 h*

S2

(4)

where

h* = 

0.5 =

sl =

is the roughness length, 

average height of obstacles, 

average drag coefficient, 

average obstacle silhouette area, 

specific area, A/N, where A is the lot area 

and N is the number of roughness elements on the lot.

This method, applied to an urban surface, states 

that an average roughness length can be computed 

from knowledge of certain fundamental characteristics. These characteristics include the average 

height of buildings, a drag coefficient, the average 

number of buildings in a crossplane exposure to the 

wind, and the obstacle density—that is, the number 

of buildings in a given area.

Estimates of surface roughness by the Lettau 

formula have been applied in only a few cases. The 

most extensive use of Lettau's method prior to this 

evaluation was in an urban energy balance study 

done at Sacramento, Calif. (Myrup and Morgan, 

1972). In Sacramento (table 2), roughness lengths 

were calculated from detailed field measurements of 

trees and buildings at 56 city locations. In another 

application of Lettau's method, Outcalt (1972b) 

used the silhouette ratio in a thermal mapping experiment along an urban-rural flight path over 

Ann Arbor, Mich. Outcalt did not, however, document roughness lengths. Other researchers report 

the use of the Lettau method in nonurban areas.

TABLE 2.—Effects of seasonal changes in vegetation on 

roughness length, by land use and land cover type, at 

Sacramento, Calif.

[Data from Myrup and Morgan (1972). Roughness lengths in centimeters!

Land use and land cover

Winter Summer

Residential areas:

Light density

Medium density

Heavy density

Shopping centers

Central business district

Office buildings

Industrial areas

Open or park areas

Parks

Freeways

36

119

123

29

321

175

10

2

2

4

108

532

370

36

321

175

13

20

127

4

Comparison of field measurements show promising 

results for the application of Lettau's methods to 

forests (Leonard and Federer, 1973) and to field 

crops (Munro and Oke, 1973).

CALCULATION OF SURFACE ROUGHNESS 

FOR BALTIMORE, MD.

THE STUDY AREA

Baltimore, Md., had a population of 905,000 in 

1970. In the same year the Baltimore Standard 

Metropolitan Statistical Area (SMSA) had a population of slightly more than 2 million, ranking llth 

in the 29 SMSA's of 1 million or more. As a study 

area, therefore, Baltimore represents a multifunctional metropolis within the belt of urbanization extending from Washington, D.C., to Boston.

The sampled area is delimited on the generalized 

land use and land cover map in figure 3. Divided 

into square-kilometer strata, the total study area is 

3 km wide, 18 km long, and covers 54 km2 or about 

one-fourth of the city surface.

The study area encompasses all major land use 

and land cover types from the industrial port area 

in the southeast, across the high-density residential 

and commercial buildup of the central business district (CBD), through parks and forest areas, to the 

lower density residential northwest area containing 

strips and clusters of moderate urban buildup.

Maximum relief of the city area ranges from sea 

level at the harbor, to slightly more than 140 m in 

the northwestern part. Local relief, however, ranges 

from less than 6 m to a maximum of 67 m within 

the 1-km2 grid cells of the study area. The greater 

local relief occurs in the fall zone near the center 

of the city and along the narrow Jones Falls Creek 

valley extending to the north-northwest. Although 

local relief may have significant effects on perturbations in the wind field (Anderson, 1971), and ultimately on the surface roughness, evaluation of topographic influence is beyond the scope of this study.

SAMPLING SCHEME

Probability sampling was used to overcome the 

practical limitations of measuring every roughness 

element in the study area. Areally stratified random 

sampling was applied to the 54 grid cells shown in 

figure 3. Because the sampling density was six 

random elements per cell, the total sample contained 

324 elements. The sample elements averaged 0.8 ha. 

The areal character of each sample reduced the 

probability of error in representation that is typical 

of the traditional point sample. Sampling was dense
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C 7

Residential center 

High intensity residential 

Moderate intensity residential 

Low intensity residential 

Commercial area 

Industrial area 

Community resource area 

Natural resource area After Wallace (1970)

Baltimore City 

Department of Planning

FIGURE 3.—Baltimore city land use and land cover map with study area delimited.
C8 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

enough that each land use and land cover type was 

included.

PILOT STUDY FOR ACQUISITION OF 

DIMENSIONAL DATA

Three kinds of dimensional data were required 

for buildings as the roughness elements on sample 

plots: (1) their height (2) their silhouette areas, 

and (3) the density of their field. The sensitivity and 

limitations of the Lettau formula, with regard to 

the economical acquisition of building and plot dimensions, were studied at a 1.6 ha residential plot 

about 3.2 km northwest of the downtown center. 

Figure 4 is an isometric drawing of the buildings on 

this plot, referred to here as the Druid Hill test 

plot.

The results of the test plot study were used to 

compare the efficiency of retrieving data from different sources so that dimensioning procedures compatible with the Lettau formula could be standardized. Data sources included Sanborn insurance

PLOT SIZE: 131 meters X124 meters=16,219 meters2 (1.62 hectares) 

SPECIFIC AREA: (PLOT AREA/N) = 2,027 meters2 

AVERAGE BUILDING HEIGHT BY

FIELD MEASUREMENT: 9.3 meters 

AVERAGE BUILDING HEIGHT FROM

NUMBER OF STORIES: 9.4 meters

maps, low-altitude aerial photographs, and field 

measurements.

Sanborn maps provided the most efficient data 

source for building and plot dimensions (fig. 5). The 

Sanborn map series, commonly used by city engineering and planning departments, was available 

at scales of 1:1,200 and 1:24,000. Horizontal measurements of buildings and plot perimeters were 

transferred from these maps to individual sample 

plot worksheets similar to the one for Druid Hill 

(table 3). Worksheets facilitated the calculation of 

a weighted average silhouette area for the field of 

roughness obstacle elements dimensioned on each 

plot. Low-level aerial photographs at a scale of 

1:6,000 provided supplementary information on the 

distribution of vegetation and on the validation of 

open areas.

Heights, in feet, are shown for all nonresidential 

buildings on the Sanborn maps. Residential building 

heights, comprising a large proportion of the 

samples, are shown only by the number of stories.

EXPLANATION

Building height in meters 7.6 

Number of stories [2] 

Southwest silhouette A 

Southeast silhouette B

FIGURE 4.—Isometric drawing of the Druid Hill test plot.
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C9

TABLE 3.—Druid Hill test plot dimensional worksheet

"A" silhouette ("A" vector, 225°)

Building Length v 

No. (ft.) X

1 330

2 330

3 50

4 45

5 175

6 175

7 80

8 55

zo— 0.5(31)

1 330

2 175

3 80

4 50

Height 

(ft.) X

32

38

26

23

27

25

37

36

Total

7911

/ O/i AO \

21823

32

27

37

26

Total

7290

19058

Weight =

0.27

.27

.04

.04

.14

.14

.06

.04

1.00

0.52

.28

.13

.07

1.00

Calculations

Weighted 

silhouette 

area

2851

3386

52

41

662

662

178

79

7911

Calculations

5491

1323

385

91

7290

for entire plot "B" silhouette <"B" vector, 135°)

Building 

No.

1

2

3

4

5

6

7

8

for half plot

1

2

3

4

Length v 

(ft.) X

50

70

70

70

70

70

45

45

««= 0.5(31)-

50

45

70

70

2.-— 0.5(31)

Height 

(ft.) X

32

38

37

36

26

23

27

25

Total

1963

21823

37

27

26

37

Total

1943

/ O/l A O \

19058

Weight =

0.10

.14

.14

.14

.14

.14

.11

.10

1.00

0.21

.19

.30

.30

1.00

Weighted 

silhouette 

area

160

372

363

353

255

225

122

113

1963

389

231

546

111

1943

Building heights in the Druid Hill test plot were 

calculated from trigonometric solutions to clinometer 

measurements. After field measurements of a range 

of residential building types, reliable building 

height information was subsequently determined 

from the number of stories. Sensitivity testing of 

height values in the Lettau formula (eq. 4) showed 

that precision clinometer measurements improved 

the accuracy of the roughness lengths by only a few 

centimeters compared to indirect height estimates 

from the number of stories.

The determination of the average building silhouette area for a given plot also required the development of a particular methodology. As shown in 

figure 4, rectangular buildings have two distinctive 

silhouettes, designated "A" and "B" hereafter.

An "effective silhouette area" as a function of the 

wind azimuth, also needs to be defined. In only two 

unique cases (and their reciprocal exposures) was 

the wind perpendicular to either the A or B silhouettes. Variability of the azimuth of urban surface winds (Graham, 1968) indicates that winds 

will most likely intercept portions of both silhouettes. It was therefore necessary to calculate two 

average silhouette areas for each plot and to sum 

the effective areas of each.

Unless all roughness elements were of similar 

size, simple numerical averages of A and B silhouette areas sometimes produced results that did not 

represent the average silhouette. Significant size 

differences within a heterogeneous mix of buildings 

indicated that each structure does not have equal 

influence on the aerodynamic process. Proportional 

weighting of individual building silhouette areas 

overcame this problem. Table 3 shows the worksheet 

calculations of the weighted silhouette areas of 

structures in the Druid Hill test plot. Weights were 

determined by ratioing the individual silhouette 

area to the sum of the silhouette areas.

An assessment was made of the minimum plot 

size necessary to ensure representative roughness 

lengths in a given sample. It was found that test 

plots with relatively homogeneous building geometry, such as the Druid Hill test plot, could be reduced in size without a loss of information. Calculations of building silhouettes based on data from 

slightly less than half the original sample area— 

that is, four buildings on 0.7 ha rather than eight 

buildings on 1.6 ha—are shown in the worksheet 

(table 3). Depending on the wind azimuth, roughness lengths calculated from data for the small area 

differed only 5 to 10 percent from those calculated 

from data for the entire area.

When building geometry in a sample plot is heterogeneous, particularly in a central business district where building geometry abruptly changes
CIO THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

FIGURE 5.—Sanborn map of the Druid Hill test plot.

within short distances, the minimum plot size must 

be set carefully. To accurately represent a segment 

of such a heterogeneous surface, both extremes of 

the geometric variation in a single plot must be 

characterized. For example, the highest roughness 

value calculated was for a 1.4 ha-sample plot containing two 35-story buildings intermixed with 

three other buildings ranging from only 2 to 7 

stories. If a plot containing only one of the 35-story 

high-rise buildings had been sampled separately, the 

calculated roughness length would have been 63 percent higher, and would not have represented the 

entire field of roughness elements. Conversely, if 

the plot size is excessively large, it will overgeneralize rather than actually measure the true local 

influence of the building geometry.

Delimiting the perimeter of a sample areal plot 

may also influence the calculated roughness length. 

Table 4 shows the effect of different lot sizes in a

TABLE 4.—Relation between plot size and roughness 

length

fNumber of buildings on plot: 8,

average silhouette area: 720 ftj

average building height: 36 ft]

Plot

(ft)

100 X 200

200 X 200

200 X 300

300 X 300

Total areas

(ft3 )

20000

40000

60000

90000

Specific area

(ft2 )

2500

5000

7500

11 250

Zo

(cm)

156

78

52

35
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C 11

hypothetical plot containing eight residential buildings. A linear relationship exists between the plot 

area and the calculated roughness length.

Street centerlines were used as the boundary between adjacent built-up plots within the urban area. 

Careful judgment, however, is required to determine the boundaries of a sample area when they are 

ill-defined. In the case of a typical shopping plaza, 

for example, it was necessary to determine whether 

parking lots should TDC included with the building 

complex. With land use and land cover as a criterion, the parking areas were considered inseparable 

from the building complex, since a typical plaza 

contains both. In other cases where the perimeter 

was less well defined, the principal criterion for delimiting the plot was how much area was necessary 

to adequately represent the actual surroundings.

TESTING THE EFFECTS OF WIND VARIATIONS ON 

BUILDING SILHOUETTES

Rectangular buildings are very sensitive to the 

azimuth of wind attack and their effectiveness as a 

roughness obstacle is significantly reduced when 

winds are not perpendicular to the silhouette face. 

This relationship may be inferred from the A and 

B silhouettes of the buildings illustrated in figure 4. 

A wind shift away from the direction perpendicular 

to silhouette A decreases the effectiveness of silhouette A as a roughness obstacle. At the same time, 

although the shift increases the effectiveness of silhouette B as an obstacle, because the wind is more 

perpendicular to B, the wall area of B still presents 

a much smaller face than A.

The change in silhouette exposures occasioned by 

shifts in wind direction raises several questions regarding the calculations of surface roughness from 

an obstacle element description: (1) is the wind 

direction significant in the computation of roughness length? (2) are all building shapes sensitive 

to the wind azimuth? and (3) what are the parameters in the evaluation of the effect of the azimuth?

A method for mathematical rotation of each 

sample plot containing buildings was devised to 

test the effect of different wind azimuths on calculated roughness lengths. Starting with a vector 

normal to one of the building walls, a 90° rotation 

offers all possible direct or reciprocal exposures to 

the wind. The wind direction was changed in steps 

of 15° to give a total of seven calculated roughness 

values for each sample plot. Continued rotation 

would not provide additional information because 

the changes in silhouettes would be merely repeated 

as reciprocal exposures.

Orientation vectors for the A and B silhouettes 

were determined along with the building and plot 

dimensions. The orientation vector is defined as the 

perpendicular to the silhouette face, expressed as 

an azimuth. Each sample plot, therefore, has its own 

"A vector" and "B vector" to describe its orientation to a prevailing wind direction.

Plot rotation was accomplished mathematically 

by specifying the total silhouette area exposed to a 

particular wind azimuth; then calculating the 

roughness length at each increment of rotation. As 

shown in figure 6, there are two unique cases in a 

90° rotation when the wind intercepts either the A 

or B silhouettes perpendicularly. Winds at all other 

azimuths intercept only portions of each silhouette.

The total effective silhouette area was calculated 

by adding the individual contributions of the A and 

B silhouette faces. For example, the exposed portion 

of silhouette A was determined by measuring the 

angular difference between the perpendicular to the 

silhouette, or A vector, and a specified wind direction. This difference is called a silhouette depletion 

factor, or angle A. The effective exposure of the A 

silhouette face was then calculated as the cosine of 

angle A.

For example, if the wind azimuth in a test plot 

is the same as the A vector, the difference will be 

0° and the cosine 1.0. Therefore, the A silhouette is 

not depleted when the wind is perpendicular. Because cos 45° = 0.7071, the A silhouette area declines 

to 71 percent of its original value when the wind 

intercepts the silhouette at an azimuth of 45° from 

the perpendicular or A value. The exposure for the 

B silhouette is calculated similarly, then added to the 

A portion to specify the total exposed silhouette as 

a function of the wind direction.

Figure 6 illustrates the effects of changing wind 

azimuths on the roughness values of the Druid Hill 

test plot. Starting with the perpendicular to the A 

silhouette face at wind azimuth 225°, the first 30° 

of plot rotation (from 225° to 195°) only slightly influences the roughness length. With the next 30° 

of rotation (from 195° to 165°), the roughness 

length decreased to 71 percent of its initial value. 

In this segment of rotation, the effect of A silhouettes begins to diminish more rapidly, whereas the 

appearance of the B silhouettes in the wind field 

brings in relatively smaller interception areas. The 

final 30° of plot rotation (from 165° to 135°) results 

in the most significant reduction in the roughness 

length. All of the larger A silhouettes are depleted, 

leaving only the smaller B silhouettes exposed to
C 12 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS 

EXPLANATION

Wind azimuth 225°

Roughness length 171cm

Southwest silhouette A

Southeast silhouette B

225

171 cm

135° 

42 cm

210° 

176 cm

150° 

85 cm

195° 

170 cm

165° 

122cm

FIGURE 6.—Effects of wind direction on surface roughness.

the wind. The final roughness length, after 90° of 

rotation, is one quarter of its original value.

The amount of change in the calculated roughness 

value, as a function of the wind azimuth, is related 

to the ratio of the B to A silhouette areas. If both 

silhouette areas are identical, as in the case of cubical forms, little variation in surface roughness will 

be expected from plot rotation. The set of samples 

in this study, however, contains a wide, yet representative, range of silhouette area ratios. This variation in silhouette area ratios and its effects on 

surface roughness, is assessed both statistically and 

aerodynamically.

To compare statistically the variations in surface 

roughness with the range of building configurations, 

the coefficient of variation (the ratio of the standard 

deviation of a variable to its mean) is employed.

For the Druid Hill test plot, the coefficient of variation of the roughness lengths is 0.39 and was calculated from a standard deviation of 51 cm divided 

by a mean of 131 cm. This coefficient may now be 

meaningfully compared with all others in the sample 

set to evaluate the effects of plot rotation, and thus 

the effects of wind direction on surface roughness.

The distribution of coefficients of variation of 

roughness lengths for all building plot samples is 

illustrated in figure 7. This histogram, with its 

descriptive statistics, provides a means for evaluating the statistical significance of plot rotation on 

the range of building configurations. The coefficients 

of variation ranged from 0.04 to 0.54, with a mean 

value of 0.19, resulting in a positively skewed distribution. The dispersion about the mean ranged 

from 0.08 to 0.30, corresponding to minus and plus 

one standard deviation.
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C 13

50

45

40 -

35 -

30

o

2

LU

Z> 25

O

LU

cc

20 -

15

10

Q.08 0.19 0.30 

1 Standard deviation (s) Mean (X) +1 Standard deviation (s)

t

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 

COEFFICIENT OF VARIATION (s/X)

0.45 0.50 0.55 0.60

FIGURE 7.—Histogram of coefficients of variation of roughness lengths, from plot rotation.

An examination of the geometry of the buildings 

in the samples beyond the one standard deviation 

portion of the distribution (coefficients greater than 

0.30) revealed the presence of elongated structures 

such as exceptionally long row houses, and warehouses. Typical width-to-length ratios of these 

buildings were 0.16 and smaller. When this ratio 

exceeded 0.22, the building geometry was represented by coefficients of variation of the roughness 

within the one standard deviation portion of the 

histogram (coefficients of 0.30 and smaller). The 

transition between the two regions of the distribution was characterized by a width-to-length ratio of 

approximately 0.20. The statistical variation was, 

therefore, found to be related to the properties of 

the buildings.

Further examination of sample plots with coefficients of variation less than 0.30, or within one

standard deviation of the mean, showed that some 

sample plots had buildings with small width-tolength ratios. This apparent anomaly of small 

width-to-length ratios, without the expected high coefficient of variation of roughness lengths, was explained by the particular plot orientations. All these 

anomalous plots had buildings arranged in quadrangle or L-shaped configurations. As the long silhouette of one set of buildings is diminished with a 

shift in wind azimuth, the silhouette area reduction 

is compensated for by the simultaneous increase 

in the long silhouettes of their perpendicularly 

oriented companion buildings.

This type of sensitivity analysis showed that for 

a wide range of building geometries and layouts in 

262 sample plots, only 16 percent were significantly 

sensitive to varying wind azimuths. Although 16 

percent of the plots appeared statistically sensitive
C14 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

to changes in the wind azimuths, the actual variability of surface winds suggests that the mean 

roughness length calculated from values acquired 

by the plot rotation is an acceptable generalization. 

The unlikelihood that a wind would be sustained 

from a single direction is verified by an analysis 

of urban winds at Fort Wayne, Ind. (Graham, 

1968). Graham found that near the city center, one 

standard deviation of the azimuth angle of the winds 

was 20°, or a total variation of 40° about the mean 

azimuth. These findings provide confidence that the 

mean value of surface roughness determined from 

plot rotation is sufficiently representative to be 

mapped.

MODIFICATIONS AND PROCESSING OF DIMENSIONAL

DATA

One of the primary objectives of this study was to 

evaluate building geometry as a means of indirectly 

estimating the aerodynamic properties of urban 

land use and land cover classes. The urban surface, 

however, contains a variety of forms other than 

buildings. A significant proportion of the sample 

plots are without buildings and therefore require 

separate consideration. Table 5 shows data on 

sample plots according to the type of plot cover 

and formula used to describe surface roughness.

TABLE 5.—Number and percent of sample plots, by type 

of cover and surface roughness formula used

Plot 

cover

Buildings 

Buildings & trees 

Woods 

Open areas

Total

No. of 

samples

199 

63 

30 

32

324

Percent

61 

20 

9 

10

100

Formula 

used

Lettau 

Lettau modified 

Kung modified

n

1 Value assigned

Sixty-one percent of the sample plots contained 

buildings and other urban structures directly compatible with the dimensional requirements of the 

Lettau formula (eq. 4). An additional 20 percent 

of the plots contained buildings intermixed with 

rows and clusters of trees. The increase in aerodynamic roughness caused by trees was accounted for 

in the Lettau formula by an increase of the drag 

coefficient in proportion to the amount of tree cover. 

The proportional, or linear, relation between obstacle density and roughness length was previously 

determined (table 4). A field surveillance of tree 

heights and frequency of occurrence in rows was 

used to establish the categories shown in table 6.

TABLE 6.—Drag coefficients for different numbers of tree 

rows and tree heights

Drag 

coefficient

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

Average no. 

of tree rows

1

1

2

2

3

3

4

4

Tree height 

(m)

<io

>io

<io

>io

<io

>io

<io

>io

The average drag coefficient of 0.5 in formula (4) 

was increased in each of the eight steps of increasing roughness. Although the method is crude, 

the results approximate the probable linear increase 

of roughness values up to a maximum. This procedure avoids the dimensioning of tree silhouette 

areas, a problem beyond the scope of this study.

To test the use of the modified drag coefficient, a 

pilot study was performed on a low-density residential plot containing tree-lined streets. The distribution of trees, determined from aerial photographs 

and field observations, placed the sample plot in the 

class of four tree-rows with average heights greater 

than 10 m (table 6). The roughness length of 59 cm 

calculated for this plot showed that inclusion of 

tree rows produced a fully roughened surface that 

was nearly double the original value of 31 cm for 

buildings alone. The results are conservative when 

compared to the value of 96 cm calculated for a 

wooded plot with tree heights averaging 10 m by the 

crop regression method described below.

Wooded plots, comprising 9 percent of the sample, 

did not lend themselves to the geometric dimensioning required by the Lettau method. Instead, a regression equation devised by Kung (1961) was employed for estimating the roughness lengths of tall 

crops. The regression equation relating plant height 

to the aerodynamic roughness of evenly spaced 

crops is:

log,,, z,, = a + b logui h (5)

where a and b are empirically determined constants, 

and h is the average crop height.

The constants a and b in the equation are -1.24 

and 1.19, respectively. These values are in good 

agreement with those derived by numerous other 

investigators for vegetated surfaces ranging from 

short grasses to tall trees (Sellers, 1965).

Aerial photographs revealed that the distribution 

of vegetation in the wooded sample plots varied 

greatly. Sample plots 0.4 ha or larger were classi-
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE CIS

fied as wooded if 30 percent or more of the area was 

covered by trees. Average tree heights were determined from field data.

The fourth group of cover types listed in table 5 

is open areas with no significant building or tree 

obstacles. Open areas are 10 percent of the total 

number of samples and consist of grass, wide highways, brush and other low vegetation, and relatively 

empty railroad yards. In addition, some samples 

represented lakes or estuaries. Because no technique 

was available to deal with these open areas, roughness lengths were assigned to each sample plot according to a description of similar surfaces for 

which field measurements were cited in the literature. Table 7 lists the different types of open area 

surfaces, their assigned roughness lengths, and 

their source references.Table 8 summarizes the procedure for organizing the surface description data 

according to the three principal cover types and 

associated formulas described above.

BASIC DATA DESCRIPTION

The frequency distribution of all the 324 roughness lengths is plotted as a histogram in figure 8. 

The shape of the histogram shows that the distribution is bounded, that is, no negative or zero values 

are in the sample. The high positive skewness reflects the extreme values of surface roughness derived from the samples from the CBD. The deparTABLE 7.—Roughness lengths, in centimeters, assigned to 

open areas

Type of 

surface

No. of 

occurrences

Assigned Source

Estuary 11 1 

Small lakes 2 2

Grassy shoreline 1 4

Grassy field 2 5

Tall weeds 2 8

Streets & 48 

sidewalks

Railroad yards 4 8 

Brush 3 14

Walls & low 1 30 

vegetation

Plate (1971). Values from 

0.03 cm to 0.3 cm. Set 

to 1 cm for convenience.

Maisel (1971) From field 

measurements in Columbia, Md.

Plate (1971). For grasses 

30 cm high.

Slade (1968). From a 

value for lawns.

Maisel (1971). Field 

measurements.

Maisel (1971). Field

measurements of streets 

prior to development.

Maisel (1971). From, 

similarity to streets 

and sidewalks.

Szeicz and others (1969). 

For brush with heights 

of 135 cm.

Rosenberg (1966). From 

field measurements of a 

210 cm high shelter.

ture from symmetry in the distribution is, therefore, 

explained by the extremely large silhouette areas 

of high-rise buildings. Statistics for the entire 

sample are summarized in table 9.

TABLE 8.—Procedure for organizing surface description data

Type of cover

Number of samples

Percent of total

Data sources:

Sanborn maps

aerial photographs

zoning maps

field checks

Data collected

Buildings and trees

262

81

X

X

X

X

Horizontal dimensions of buildings

and plot

Vertical dimensions of buildings

Plot orientation

Woods

30

9

X

X

Plot size

Tree heights

Open areas

32

10

X

X

Plot size

Surface description

Preprocessing 

calculations

(largely manual)

Calculations

(largely by computer)

Output

Area:

Specific area (S,.) 

Average A silhouette area

Average B silhouette area

Average building height (h*)

Average drag coefficient 

Preprocessed data in computer

program for plot rotation and

z0 calculations 

Ready to map mean z« derived

from plot rotation

Average heights 

Average density 

(zn from Kung 

formula (5))

Kung estimate modified 

according to density

Ready to map modified 

Kung estimate

Surface description 

matched to case studies

Ready to map best 

estimate
C16

100

THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

// ^

90 -

80 -

70 -

60

o

z

LU

D 50

OLU 

DC 

U_

40

30

20

10

LU

Q

Q <» 

oc PO

< Z 

O <

Y

P

^^

100 200 300 400 500 600 700 800

ROUGHNESS LENGTH, IN CENTIMETERS

FIGURE 8.—Histogram of 324 roughness lengths.

y/-

900 1,000 1,500

TABLE 9.—Summary of roughness length data in 

centimeters

[Sample size is 324 |

Statistic

Maximum

Minimum

Range

Mean

Median

Standard deviation

Range after 8 highest

values removed

1468

1

1467

102

70.5

138

416

DESCRIPTIONS OF LAND USE 

AND LAND COVER CLASSIFICATION SYSTEMS

The three land use and land cover classification systems used to evaluate the regional distribution of 

surface roughness in Baltimore are: (1) U.S. Geological Survey Level II land use and land cover 

classification system (Anderson and others, 1972); 

(2) city of Baltimore Zoning Ordinance System; 

and (3) an experimental classification scheme for 

physical-environmental applications patterned after 

the U.S. Geological Survey system and referred to 

herein as Baltimore Level III.
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C 17

TABLE 10.—Land use and land cover in Baltimore according to three different classification systems

[Values ai-e expressed as a percentage of the total sample)

uses

Level II

Residential (11) 48.5

Commercial & Services (12) 15.0

Industrial (13) 10.2

Transportation,

Communications,

& Utilities (15) 10.2

Institutional (16) 3.7

Water (51-54) 3.7 

Deciduous (41),

Evergreen (42), & 

Mixed Forestland (43) 3.7

TOTAL 100.0

Baltimore 

Zoning Ordinance

Residential :

R-12 2.9 

R-34 5.2

R-50 8.0

R-60 10.6

R-70 9.0

R-80 16.1

51.8

OfficeResidential: 

OR-2 3.9

OR-4 1.9

5.8

Commercial:

B-12 8.0

B-30 5.8

B-45 3.2

17.0

Industrial:

M-10 3.2

M-20 5.2

M-30 17.0

25.4

TOTAL 100.0

Baltimore 

Level III

Residential:

RLD2 1.9 

RLD2W 8.6

RMD2 13.3

RMD2W 5.9

RMD3 6.5

RMD3W 2.8

RMD4 2.8

High Rise, HR 

Commercial :

CLD 1.5 

CMD 8.6

Institutional,

INST

Open Areas,

OPEN

Industrial :

ILD 12.0

IMD 4.6

IHD 3.1

Wooded:

WLD 4.0

WMD 4.0

WHD 1.9

TOTAL

41.8 

4.9

10.1

3.7

9.9

19.7

9.9

100.0

The percentage of land use and land cover in 

Baltimore organized according to the categories of 

land use and land cover in the three classification 

systems are shown in table 10. These percentages 

compare favorably with the proportions of land use 

and land cover in other large metropolitan areas 

(Abrams, 1965) as shown in table 11. Differences in 

the categories reflect the needs of different users. 

In many cases the differences are only those of 

scale and result from either the establishment or

TABLE 11.—Land use and land cover in selected cities

[Adopted from Abrams (1965). Values expressed as a percentage of the 

total area of each city]

Category

Residential

Commercial

Industrial & 

transportation

Public rijrhtsof-way

Institutional

Open space & 

recreational

Detroit

47.0

2.6

9.3

31.5

4.5

5.5

Pittsburgh

51.0

1.8

9.0

27.9

3.9

6.1

Phila. 

delphia

52.0

4.2

11.8

16.6

8.5

6.6

Clevelanfl

44.0

4.9

9.0

23.0

4.0

14.8

Average

48.5

3.4

9.8

24.8

5.2

8.3

aggregation of subclasses. Each of the land use and 

land cover systems in table 10 is discussed below.

USGS LEVEL II SYSTEM

The U.S. Geological Survey land use and land 

cover classification system is a two-level scheme, 

hierarchically designed to facilitate more detailed 

levels of categorization (Anderson and others, 1972 

and 1976). Level I is intended for use at extremely 

small scales and is too generalized to be applicable 

to this mesoscale problem. Level II categories are 

designed for use at a scale of approximately 

1:100,000 and are similarly too generalized. A further categorization, extrapolated from Level II for 

use at larger scales, is introduced in the succeeding 

section. Hierarchically, these more detailed categories can be designated Level III.

Land use and land cover boundaries in Baltimore 

mapped at Level II are illustrated in figure 9. 

Sample strata of the study area are superimposed 

over the land use and land cover boundaries. Table 

12 is a partial list of the 34 Level II land use and 

land cover categories defined by Anderson (1972).
CIS THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

FIGURE 9.—Baltimore land use and land cover categories defined by USGS Level 

II classification system. Numbers identify the categories described in the table 

of USGS Level II classes used in this study. The gridded part of the map is 

the 54 km2 study area.
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C 19

TABLE 12.—USGS Level II land use and land cover classes 

applicable to this study

[Data from Anderson and others (1972) |

(Level I: 1 Urban & Built-up Land)

11 Residential

12 Commercial & Services

13 Industrial

14 Extractive

15 Transportation, Communications, & 

	Utilities

16 Institutional

17 Strip & Clustered Settlement

18 Mixed

19 Open and Other

(Level I: 2 Agricultural Land)

21 Cropland & Pasture

22 Orchards, Groves, Bush Fruits, Vineyards, & Horticultural Areas

(Level I: 4 Forest Land)

41 Deciduous Forest

42 Evergreen Forest

43 Mixed Forest

(Level I: 5

51

52

53

54

Water)

Streams & Waterways

Lakes

Reservoirs

Bays & Estuaries

Nine of these categories were identified within the 

Baltimore study area (table 12a). Physical, and 

thus aerodynamic, similarity between classes such

TABLE 12 a.—USGS Level II classes used in this study and 

their roughness lengths

Roughness length, cm

Class (abbreviation) Mean Median

11 Residential (RESID) 100 88

12 Commercial and Services 181 107 

(COMML)

13 Industrial (INDUS) 52 36 

15,1J> Transportation, Open and 47 29

Other (TRANS)

16 Institutional (INSTI) 130 62 

41 Deciduous Forest 117 106

(WOODS) 

53,54 Reservoirs, Bays and 1 1

Estuaries (WATER)

as 53 and 54 (Reservoirs, Bays and Estuaries) permitted their integration and thereby reduced to 

seven the number of categories used in this study.

BALTIMORE ZONING ORDINANCE SYSTEM

The most detailed land use and land cover system 

currently available for Baltimore is the Baltimore 

Zoning Ordinance (City of Baltimore, 1971) System. This system has four major categories: Residence (R), Office-Residence (OR), Business (B), 

and Industrial Districts (M). These categories are 

subdivided into 19 intermediate classes; for example, R-l, R-2, OR1 and OR2. Nonresidential 

classes are aggregated in a third level of the hierTABLE 13.—Baltimore Zoning Ordinance classes according to allowable gross building densities

Combined 

zoning Land use and land cover 

zoning classes

No. of units 

allowable

Sample 

Roughness length, cm

Mean Median

	Residence District:

R-12 R-l Single Family; R-2 General Residence 6

R-34 R-3 Single Family; R-4 General Residence 9

R-50 R-5 General Residence 17

R-60 R-6 General Residence 29

R-70 R-7 General Residence 40

R-80 R-8 General Residence 58

Office-Residence District:

OR-29 OR-2 Office-Residence; R-9 General Residence 79 

OR-34 OR-3 and OR-4; Office-Residence 218

Business District:

B-12 B-l Neighborhood; B-2 Community Business 40-79 

B-30 B-3 Community Commercial 9-79 

B-45 -B-4-1 Central Business District;

B-4-5 Central Commercial District 363

70

78

92

95

104

121

202

324

108

119

293

60

72

88

69

83

106

185

275

76

47

148

'M-10

'M-20

'M-30

Industrial District:

M-10 Industrial, nuisance-free to adjoining

Business or Residential _____

M-20 Industrial, General Manufacturing and

Industry, moderate nuisance characteristics

M-30 Industrial, heavy industry

84

54

51

73

49

38

1 Minimum of three aci'es - Classes aggregated to form Districts
C20 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

arehy; for example, B-4-1, Central Business District, and B-4-5, Central Commercial District.

The Zoning Ordiance is meant to establish land 

use and land cover criteria that will help reduce 

overcrowding and congestion and thus enhance the 

health, safety, and services of the public. One of its 

major criterion is gross building density, a characteristic related to urban surface roughness. By 

aggregating those land use and land cover classes 

that have similar densities, the authors of this study 

formulated the 14 combined zoning classes listed in 

table 13.

BALTIMORE LEVEL III SYSTEM

Level III has not yet been formally developed in 

the U.S. Geological Survey land use and land cover 

hierarchy. Criteria for its use at meso- and microscales remain to be established by users. For regional planning activity-oriented functions must be 

emphasized when defining land use and land cover. 

But for climatological applications, the thermal, 

radiative, hydrologic, and aerodynamic properties of 

the surface must be accurately quantified. The Baltimore Level III system developed for this study constitutes a tentative land use and land cover classification system emphasizing morphological and 

physical characteristics. Thus, although the Baltimore Level III system is related to the USGS 

hierarchy, these urban morphology criteria have 

been devised for the experimental applications made 

during our research.

The basis for this Level III land use and land 

cover classification system is the differentiation of 

urban morphology using obstacle size and density 

as the principal criteria. As shown in table 14, the 

seven major categories of the Level III system are 

subdivided into 18 classes, which are described.

SPATIAL DISTRIBUTION OF SURFACE 

ROUGHNESS IN THE STUDY AREA

The areal distribution of surface roughness and 

the sample data for the study area are illustrated 

in figure 10 A, B, and C. Use of the contouring option of the Synagraphic Computer Mapping Program (SYMAP) (Dudnick, 1971) provided an opportunity to compare visually the inferred roughness field with known land use and land cover distributions. This computer mapping program printed 

an index map depicting the 54 individual 1-km- cells 

of the study area in relation to the Baltimore City 

boundary (figure 10A). It also printed a map of 

surface roughness values corresponding to each 1- 

km2 cell (figure 10C). This map is divided into two

TABLE 14. — Baltimore Level HI land use and land cover 

classes and their roughness lengths

[Developed for experimental application to an urban climate]

Sample 

Class definition Roughness length, cm

Residential category:

RLD2 Low-density, up to two stories, single units

RLD2W Same as RLD2 but with tree

rows

RMD2 Medium-density, up to two

stories, multiple units

RMD2W Same as RMD2 but with tree

rows

RMD3 Medium density, up to three

stories, multiple units

RMD3W Same as RMD3 but with tree

rows

RMD4 Medium density, up to four

stories

High-Rise category:

HR High-rise towers, five stories

or more; office, residential

or commercial, but not institutional or industrial

Commercial category:

CLD Low density (plazas or equivalent)

CMD Medium density, up to four

stories

Institutional category :

INST Institutional buildings (hospitals, colleges) but does

not include land with vegetation (wooded or open)

Industrial category:

ILD Low density, up to two stories; for example, warehouses

IMD Medium density, up to four

stories

IHD High density, over four

stories

Open Areas category :

OPEN Water surfaces (lakes, bays)

or open land less than 30

percent wooded

Woodlands category:

WLD Low density; for example,

widely spaced trees

WMD Medium density; for example, some clustering

WHD High density; for example,

full forest

Mean

38

76

73

120

113

178

147

481

11

87

162

28

78

161

7

48

116

246

Median

39

73

71

86

114

185

151

322

11

78

155

28

82

126

8

49

115

260

parts in order to show conveniently the entire study 

area on a single page. The location of sample points 

is shown by a printed surface roughness value. Additionally, the program produced a histogram of the 

mapped values (figure 10#).

The most distinctive regions on the contoured 

roughness map (fig. 10C) are those with very high
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C21

and very low values. That the lowest values, representing water surfaces, portray a realistic pattern 

of the inner harbor area can be seen by comparing 

cells A-03, A-04, and A-05 of the roughness map 

with the same cells on the Level II land use and land 

cover map in figure 9. The similarity verifies the 

adequacy of the sample distribution for representing a reasonable mesoscale pattern. The low values 

representing Druid Hill Lake in cell B-ll, and 

Pimlico Racetrack in cell C-16, are also revealed in 

the contoured roughness map.

The highest values, illustrated by class interval 

10 (from fig. 105) on the roughness map, identify 

the CBD and its peripheral clusters of high-rise 

buildings. Part of cell A-07 includes the northeastern half of the CBD and contains the highest value 

in the sample (1,468 cm).

The wide range of roughness values within cells 

A-08 and B-08 reflect a mixture of commercial, 

office-residential, and institutional complexes. The 

Level II land use and land cover map (fig. 9) shows 

some spatial correlation with the map of surface 

roughness (fig. 10C) due to contrasts of land use 

and land cover in the cells of the roughness map.

An exception to the exclusive relation of higher 

values of roughness to the CBD and its periphery, 

is in cell A-01, which shows a roughness length of 

557 cm. Such an unexpectly high value identifies a 

grain elevator complex exceeding 300 meters in 

height. Field checks verified that this feature is a 

significant high-rise cluster in an area of otherwise 

low density buildup.

Elsewhere in the study area, intermediate ranges 

of roughness values are difficult to associate specifically with a given land use and land cover category. These values will be evaluated physically and 

statistically in following sections of this study.

STATISTICAL ASSOCIATION BETWEEN LAND

USE AND LAND COVER AND AERODYNAMIC

ROUGHNESS

Table 15 is a summary of the objectives and techniques employed in the statistical evaluation of the 

relation between land use and land cover and aerodynamic roughness. Each test in the table is numbered for reference to this study's discussion. In test 

1, each land use and land cover classification system 

was evaluated separately by use of the analysis of 

variance. This first test determined whether surface 

roughness could be used to distinguish differences 

between the classes of a given land use and land 

cover system. Test 2 was made to determine which 

land use and land cover classification system most 

efficiently discriminated between its classes. Test 3 

repeated the analysis of variance, but with a nonparametric equivalent. The nonparametric version is 

an alternative requiring less rigorous assumptions 

about the form of the data. Once a land use and land 

cover system was found to possess class differences, 

test 4 permitted a pair-by-pair comparison to determine which particular classes were responsible for 

the differences.

In the first test, the null hypothesis of no difference between the surface roughness values of land 

use and land cover classes was tested by the oneway analysis of variance. This test was repeated for 

each of the three land use and land cover systems 

being evaluated, and each resulted in an F-statistic 

above the critical value at the 95-percent confidence 

level. The null hypothesis of no difference between 

means was, therefore, rejected. Test I shows the 

probability that some land use and land cover 

classes have significantly different surface roughness characteristics.

TABLE 15.—Statistical testing techniques used in land use and land cover analysis

Test Objective Technique Statistic Method source

Test hypothesis of no difference between the class 

means

Compare efficiency of land 

use and land cover classification systems by proportion of between-group 

variance

Test hypothesis of no difference between the class 

means

Determine which classes 

have significantly different means

One-way analysis of variance (parametric)

One-way analysis of variance (parametric)

Kruskal-Wallis one-way analysis of variance (nonparametric)

Q-test

F King, 1969

E Dixon and Massey, 1969

Siegel, 1956

D Snedecor and Cochran, 1967
C22 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

SURFACE ROUGHNESS

Of THE 

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SHO«N *BDV£

BlI ClO

C

BIO 

Ml B09 C06

C

B07 CO*

 07 BO* t

MMIMMMMMIMHMIIIIIMMtll

FIGURE 10.—Surface roughness in Baltimore 

study area. A (above left), Index map of 

grid cell locations relative to Baltimore city 

boundaries; B (below left), Frequency distribution of mapped surface roughness values; and C (facing page), Contour map.

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C24 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

TABLE 16.—Comparison of the variance ratio for three 

land use and land cover systems

Classification system

USGS Level II 

Baltimore Zoning Ordinance 

Baltimore Level III

No. of 

regions

7 

14 

18

^-statistic

0.102 

.166 

.531

The second test was used to compare the land use 

and land cover classification systems for their probable efficiency by calculating the E'-statistic, or variance ratio—the ratio of the between-group variance 

estimate to the total variance estimate. A comparison of the E'-statistic for each land use and land 

cover classification system is shown in table 16 in 

rank order. The Level II system showed the highest 

efficiency. The significance of this efficiency is evaluated separately for each land use and land cover 

system.

In evaluating the descriptive statistics, certain 

properties identified in the histogram of the sample 

set suggest a nonnormal distribution. Some nonnormal distributions represent a process rather than 

an unreliable sampling. Short-term precipitation, for 

example, may be characterized by a Poisson distribution (number of storms in a given time interval) 

or a Gamma distribution (precipitation amounts). 

However, since every assumption about the data 

may not have been rigorously met for the one-way 

analysis of variance, a nonparametric equivalent 

was performed. Computing the Hkw. statistic for the 

Kruskal-Wallis test, the critical value was exceeded 

at the 5-percent significance level for each land use 

and land cover system. Thus, the Kruskal-Wallis 

test, with an efficiency close to that of the more 

powerful F-iest (Siegel, 1956), also showed that 

statistically significant differences exist between the 

mean surface roughness values in each system.

A final statistical analysis was made to determine 

which particular land use and land cover classes 

were creating the between-group difference indicated by the analysis of variance. A pair-by-pair 

comparison of the mean values of surface roughness requires that a "least significant difference" 

level be established. The literature, however, is not 

clear on the method for setting a statistically significant difference level. Regarding this matter, 

Downie and Heath (1959) state:

Suppose that an F-test had been significant. Should 

we then proceed and make a series of £-tests to see just 

what groups differ from each other? In the statistical

literature there tends to be confusion on the answer to 

this question. In general, it appears that it is best not 

to. One might look at the various means and carry on 

further research using the observed differences that 

appear to be large enough to be significant.

Although Downie and Heath suggest the simple use 

of large observed differences in an evaluation, a 

least significant difference was used here to define 

a tentative boundary.

A critical value, D, is calculated using the square 

root of the within-group variance estimate, s2 , divided by the average number of observations, N, as 

follows:

/N (6)

where Q is the "Studentized Range" (Snedecor and 

Cochran, 1967) for a given significance level, in this 

case 5 percent. Having determined a critical Dvalue, differences between means of paired land use 

and land cover categories of each classification system may be statistically evaluated.

USGS LEVEL II SYSTEM

Table 17 shows that 7 of the 21 possible land use 

and land cover pairs of mean roughness values are 

significantly different. Statistical differences in the 

paired classes are apparently due to significant aerodynamic differences. The low aerodynamic properties of a smooth water surface, for example, are 

unlike those of all other land use and land cover 

classes except the industrial and transportation 

classes which generally contain large areas of open 

land.

Although the differentiation between land use and 

land cover classes of the USGS Level II scheme is 

relatively good, its small number of categories 

weakens the scheme's effectiveness for discriminating significant aerodynamic differences within some 

classes. The residential class (11), for example, may 

encompass up to half of the total urban land area

TABLE 17.—Differences between the paired means of 

roughness lengths of USGS Level II land use and land 

cover classes

[Values set in boldface type exceed the critical value of D, where Z? = 80.9] 

RESID COMML INDUS TRANS INSTI WATER WOODS

RESID o 81 48 53 30 99 17

COMML o 129 134 51 180 64

INDUS o 5 79 51 65

TRANS 0 83 46 70

INSTI 0 129 13

WATER 0 116 

WOODS 0
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C 25 

TABLE 18.—Differences between the paired means of roughness lengths of Baltimore Zoning Ordinance classes

[Land use and land cover symbols are defined in the table 13 listing Baltimore zoning classes. Values set in boldface type exceed critical value D,

where D = 130.4]

R-12 R-34 R-50 R-60 R-70 R-80 OR-29

* R-12 0 8 22 25 34 51 132

R-s* 0 14 17 26 43 124

R-so 0 3 12 29 110

R-60 0 9 26 107

R-70 0 17 98

R-80 0 81

OR-29 0

OR-34

B-12

B-30

B-45

M-10

M-20

M-30

(tables 12 and 13). Moreover, this single residential

class in the Level II scheme does not provide for

differentiation of aerodynamic properties in the

spectrum of building silhouette sizes and densities

ranging from single-family residences to high-rise

apartments. One advantage, however, of the USGS

Level II land use and land cover system is its increasing applicability to a large number of national

metropolitan areas (Wray, 1970; U.S. Geological

Survey, 1977).

BALTIMORE ZONING ORDINANCE SYSTEM

The differences in paired mean roughness values

for land use and land cover of the Baltimore Zoning Ordinance system are shown in table 18. Although the Baltimore system has twice the number

of land use and land cover classes as the USGS

Level II scheme, there is only a modest improvement in this zoning system's efficiency in discriminating aerodynamic roughness. This indicates that

the discriminating effectiveness of the classification

system is less related to the total number of categories used and more related to the characteristics

of each individual category. The most obvious improvement demonstrated by the Baltimore system is

the characterization of high-rise structures in zoning classes OR-34 and B-45. This small improvement

over the USGS Level II classification system is supported by the higher between-group variance previously calculated in the ^-statistics in table 16.

Efficiency gained by the inclusion of land use and

land cover classes describing high-rise buildings is

somewhat offset by several other aspects of the

Baltimore zoning system. The greater detail present

in residential land use and land cover as divided

into the six medium- and low-density subclasses

still fails to distinguish significant differences in

OR-34 B-12 B-30 B-45 M-10 M-20 M-30

254 38 49 223 14 16 20

246 30 41 215 6 24 28

232 16 27 201 8 38 42

229 13 24 198 11 41 45

220 4 15 189 20 50 54

203 13 2 172 37 67 71

122 94 83 91 118 148 152

0 226 205 31 240 270 274

0 11 185 24 54 58

0 174 35 65 69

0 209 239 243

0 30 34

0 4

0

aerodynamic properties. Similarly, differentiation of

industrial and commercial land into subclasses also

fails to show significant statistical differences.

Furthermore, the Baltimore system has no specific

categories for woodlands or water areas, which

would improve discrimination. Thus, despite its

greater detail, the Baltimore Zoning Ordinance system does not appear to be significantly more applicable for determining aerodynamic differences

than the USGS Level II classification system.

BALTIMORE LEVEL III SYSTEM

The Baltimore Level III classification system,

formulated to differentiate land use and land cover

classes according to obstacle height and density has

those 18 classes paired by differences in mean values

(table 19). The relatively large number of pairs

showing significantly different mean values verifies

this system's improved discrimination of surface

roughness. This improvement is supported by the

high value of between-group variance to total variance shown by the calculated E-statistic in table 16.

The differences between the paired means of the

roughness lengths of the classes in table 19 may

first be compared with the expected differences in

roughness between open areas with relatively

smooth surfaces and areas with the roughest surfaces. The values of the relatively open areas

(OPEN) are significantly different from those of

the most aerodynamically rough areas, that is highrise (HR) and high-density woodlands (WHD).

Further statistical discrimination is noted between

open areas and some classes of land use and land

cover of intermediate roughness, including threestory residential-wooded (RMD3W), four-story

residential (RMD4), institutional buildings (INST),

and industrial high-density (IHD).
C26 THE INFLUENCES OP LAND USE AND LAND COVER IN CLIMATE ANALYSIS

TABLE 19.—Differences between the paired means of roughness lengths of Baltimore Level III land use and land

cover classes

[Land use and land cover symbols defined in the table 14 listing. Values in boldface type exceed critical value D, where Dr=112.4]

G a Q Q 

§

RLD2 o 38 35 82

RLD2W 0 3 44

RMD2 0 47

RMD2W 0

RMD3

RMD3W

RMD4

HR

CMD

CLD

INST

ILD

IMD

IHD

OPEN

WLD

WMD

WHD

RMD3

75

37

40

9

0

RMD3W

140

102

105

58

65

0

o 

s

109

71

74

27

34

31

0

W

443

405

408

361

368

303

334

0

o 

o

49

11

14

33

26

91

60

394

0

o 

o

28

65

62

109

102

167

136

470

76

0

H 

02

5

124

86

89

42

49

16

15

319

75

151

0

o

10

48

45

92

85

150

119

453

59

17

134

0

Q

s

40

2

5

42

35

100

69

403

9

67

84

50

0

o 

S

124

86

89

42

49

16

15

319

75

151

0

134

84

0

OPEN

31

69

66

113

106

171

140

474

80

4

155

21

71

155

0

o

10

28

25

72

65

130

99

433

39

37

114

20

30

114

41

0

o

78

40

43

4

3

62

31

365

29

105

46

88

38

46

109

68

0

0

w

207

169

172

125

132

67

98

245

158

234

83

217

167

83

238

197

129

0

Classes with the roughest surfaces, HR and WHD, 

show significant differences in value when compared 

with those in most of the other land use and land 

cover classes. The exceptions occur when a land use 

and land cover category is characterized by a roughness value similar to the value with which it is 

paired. The roughness characteristics of high-density woodlands, for example, are not significantly 

different from those of groups of the highest buildings—that is, medium-density three-story and fourstory buildings, and high-rise buildings.

The roughness values of some land use and land 

cover categories that include buildings of intermediate height and are of intermediate building 

density do not differ significantly from each other. 

The roughness values of low-density residential 

areas are not significantly different from those of 

medium-density residential areas unless the mediumdensity class contains three-story buildings and treelined streets. For example, the differences in surface 

roughness between the RMD3W and the other residential classes suggest that residential land need 

not be differentiated into more than two classes, 

with the change from low- to medium-density residential areas made beginning at the RMD3W class. 

The HR class, consisting of high-rise office and residential buildings five or more stories high, can be 

very easily discriminated without modification from 

the other classes.

The roughness values of land use and land cover 

categories in the middle portion of the spectrum of 

mean roughness values overlap those of their neighbors. For example, the medium-density industrial 

class shows significant contrast only with the classes 

that are the roughest aerodynamically: HR and 

WHD. The mean roughness values of the low-density industrial class, however, differ significantly 

from those of four other land use and land cover 

categories: RMD3W, RMD4, INST, and OPEN. 

Because land use and land cover categories with intermediate mean roughness values are difficult to 

sort out and interpret, further evaluation of them is 

deferred until their physical response is measured 

by numerical simulation of the surface climate.

THE CBD AS A UNIQUE LAND USE AND 

LAND COVER CLASS

The CBD has been characterized in the earlier 

section on statistical description and analysis as a 

unique part of the sample. This uniqueness is evident from the exceptionally high surface roughness 

values associated with high-rise buildings in the 

CBD and the expected strong influence by increased 

turbulent transfer on the surface energy balance. 

Part of the CBD is identified on the surface roughness map in figure 10C by the high values occurring 

in portions of cells A-07, B-08, A-09, and B-09. 

These high values are distributed over approximately 5 percent of the study area.

In spite of the relatively small size of the CBD 

within the city, several factors justify further attention regarding the use of this area as an independent land use and land cover class. First, in 

Baltimore as in most cities high-rise buildings typi-
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C 27

cally occur interspersed with relatively low buildings, rather than as a continuous field of high structures. Sample values are, therefore, especially sensitive to the particular building compositions of individual plots. More detailed measurements are needed 

to verify the accuracy of the small CBD sample. 

Secondly, the skyscraper portion of the Baltimore 

CBD is undergoing rapid growth with continued 

expansion projected into the coming decade. A significant part of the Metro-Center Technical Study 

and Comprehensive Plan (Wallace and others, 1970) 

is already a reality for the Baltimore inner harbor 

area. Finally, knowledge of the surface roughness 

characteristics of the Baltimore CBD may be useful for making better estimates of the surface roughness of metropolitan centers elsewhere, especially 

those possessing larger central business districts.

To verify the reliability of the previous sampling 

of the CBD area, surface roughness is calculated for 

a complete population of roughness elements within 

the central core of the district. This defined CBD 

core area is approximately 0.7 km2 and is divided 

into the 12 plots shown in figure 11. Plot identification numbers are the Sanborn map page numbers 

from which building dimensions were acquired. The 

average plot size is 5.7 ha. The average roughness 

length for each plot is also contained in the figure.

In table 20 a list of building height, roughness 

length, and silhouette ratio for each CBD areal plot, 

is presented, where the silhouette ratio is a density 

index taken from the Lettau formula (eq. 4): High 

silhouette ratios must be accompanied by significant 

building heights to produce higher surf ace roughness 

values. The highest roughness length, 2,118 cm, apTABLE 20.—Aerodynamic roughness characteristics of the 

Baltimore CBD

No. of 

CBD plot

22A

23A

24A

25A

32A

33A

34A

35A

42A

43A

44A

45A

TOTAL

No. of

Highest 

3 

buildings

7, 7, 6

20,12,12

8, 6, 4

9, 5, 3

30, 27, 22

37, 28, 28

18, 14, 13

4, 3, 2

21,14,14

30, 22, 21

15,12,10

8, 5, 5

37, 30, 30

stories

Plot 

average

4

7

4

4

14

10

8

3

7

12

8

5

8

Aerodynamic values

Roughness _.,, length Silhouette

(cm) ratl°

226

1,468

167

136

1,617

1,631

640

51

707

2,118

933

195

673

0.29

0.65

0.21

0.14

0.47

0.54

0.38

0.09

0.29

0.90

9.51

0.21

0.39

22A 

542 cm

32A 

1617cm

42A 

707 cm

23A 

1486 cm

33A 

1631 cm

43A 

2118cm

24A 

167 cm

34A 

640 cm

44A 

933 cm

25A 

136 cm

35A 

51 cm

45A 

195 cm

FIGURE 11.—Distribution of surface roughness values, in 

centimeters, in the core area of the Baltimore central 

business district (CBD). The core area is divided into 12 

plots, each designated by a Sanborn map page number.

pears in plot 43A. Viewed from the south, this plot 

contains the largest buildings in the middle section 

of the CBD including the Maryland National Bank 

Building, and the recently constructed U.S. Fidelity 

& Guaranty Building. Buildings in plot 43A average 12 stories, although four buildings exceed 20 

stories in height and another three buildings are 

over 12 stories high. Plots 32A and 33A have taller 

buildings but their heights are offset by the larger 

silhouette ratio in plot 43A. Plots on the eastern 

side of the CBD core area have the lowest roughness lengths, with plot 35A having a value of only 

51 cm. This peripheral area has low buildings (three 

stories) and considerable open land. This side of 

the CBD has a more abrupt transition between 

high- and low-density commercial land use and land 

cover.

This evaluation shows that surface roughness has 

a relatively high absolute range of values across the 

CBD and varies according to the composition of 

building heights, densities, and silhouette ratios. 

Where high-rise buildings are tightly clustered, the 

high roughness values may be overestimated because cluster shielding reduces silhouette exposures.
C28 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

Conversely, values for open areas within the CBD 

may be underestimated because of their location in 

the downstream path of turbulence induced from 

neighboring high-rise buildings. The mean roughness length for the entire population of obstacles, 

824 cm, appears to be a reasonable mesoseale estimate for the overall CBD core area. Estimates of 

the roughness length for high-rise buildings, however, represent the extension of an empirical expression that is difficult to evaluate physically (Monteith, 1973).

LAND USE AND LAND COVER CLASSES RANKED BY 

MEAN ROUGHNESS LENGTHS

Mean roughness lengths were used to rank land 

use and land cover classes within each of the three 

systems described in this paper. The rankings for 

the three systems are compared in figure 12. The 

position of each class within the spectrum of surface roughness values shows the resolution of each 

classification system in relation to the others. In 

addition to resolution—the sensitivity of each system to variations in roughness length—the efficiency 

and economy of classification effort is also suggested 

by the ranking of the classes. The degree of efficiency and economy is derived by determining the 

availability of data needed for a particular land use 

and land cover classification system and the number 

of land use and land cover classes contained in that 

system.

The seven categories of the USGS Level II classification system give a conservative estimate of the 

aerodynamic properties of land use and land cover. 

The ranked distribution of values (fig. 12) generally agrees with the physical character of the 

classes. The roughness values for Level II are conservative because a wide range of differences is 

masked in the averaging process. Small modifications to this classification system would greatly enhance its potential for distinguishing physical properties without adding undue complexity to its structure. In particular, a separate class to identify highrise structures, regardless of activity function, could 

be incorporated into the system without difficulty.

Another important shortcoming of the Level II 

system is the absence of subclasses in the residential 

land use and land cover category. Resolution would 

be greatly improved by a simple two-fold division 

between high- and low-density classes. Wray (1973) 

successfully applied such a modification to the USGS 

Level II system by dividing residential land into 

single- and multiple-family categories, thereby increasing the applicability of the system.

CLASSIFICATION SYSTEM 

Roughness Length USGS Level II Baltimore

z0(cm) Zoning Ordinance

0 I- i- 53,54

Baltimore Level III

50

100

200

300

400

500 t

15,19 -

- 13

11 -

- 41

16 -

M-20

- M-30

- R-12

R-34 -

R-60 - 

B-12 - 

R-80 -

- M-10

- R-50

- R-70

- B-30

- 12

B-45 —

- OPEN

CUD -

- ILD

RLD2 -

- WLD

RM02 — m now IMD -r- RLD2W

- CMD

RMD3 -

WMO - RMD2W

RMD4 -

IHO

- OR-2

WHO -

- OR-4

INST

- RMD3W

- HR

FIGURE 12.—Land use and land cover classes ranked by mean 

values of surface roughness. The symbols for land use and 

land cover categories of the three classification systems 

used are listed in the tables describing these systems.

The Baltimore Zoning Ordinance classification 

system, with its 14 land use and land cover classes, 

shows only modest improvement over the Level II 

classification system in the resolution of features 

related to surface roughness. It does not account for 

high-rise buildings in the office-residential classes 

(OR-2, OR-4), but includes a category for the CBD 

(B-45). The six residential classes do not clearly 

distinguish known differences in building densities. 

The surface roughness values of these six classes 

are distributed closely around the central value 

derived for the Level II residential class. In general, 

the economy of the classification effort is lost in the 

large number of categories in the zoning system.
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C29

The Baltimore Level III land use and land cover 

system, as compared with the others in figure 12, 

shows the best resolution in differentiating roughness length. It also demonstrates a stronger relationship with certain physical characteristics than 

schemes that employ strict activity function criteria 

in land use and land cover classification. Residential, 

commercial, and industrial classes show a general 

quantitative increase along the spectrum of values 

according to physical characteristics; for example, 

low, medium, and high densities of development. 

Low-density commercial land, representing large 

open shopping plazas, is similar aerodynamically to 

such land use and land cover categories as open and 

low-density industrial land. Clusters of high-rise 

buildings represent a distinctive class regardless of 

activity function, since residential, commercial, and 

institutional high-rise buildings have similar physical properties.

A comparison of the three land use and land cover 

classification systems evaluated herein shows the 

Baltimore Level III system to possess the best resolution and agreement with the physical properties of 

land use and land cover in a given class. It is not, 

however, a system widely used in mapping. The 

Level III system requires the prospective user to 

generate a land use and land cover classification for 

his region, a task beyond the capability of many investigators. The chief value of Level III lies in its 

resolution, but that superior resolution may be offset 

by economic considerations. The Baltimore Zoning 

Ordinance System has demonstrated some value for 

mapping land use and land cover but suffers from 

the redundancy of certain land use and land cover 

categories. In addition, its zoning boundaries are 

not established according to a universal set of criteria. On the other hand, wherever Level II 

mapping has been done the USGS Level II system, 

with the suggested modifications, may be used 

immediately.

EVALUATION OF AERODYNAMIC ROUGHNESS

BY SIMULATION MODELING OF SURFACE

CLIMATE

Energy balance simulation models are increasingly being used in climatology to study the interrelationships between meteorological conditions and the 

physical character of the surface. Theoretical models 

of energy transfer through the Earth-atmosphere 

interface were developed relatively early (Brunt, 

1934, Lettau, 1951). Hardware was eventually developed to model the effects of the essential atmospheric and surface parameters. Halstead and others

(1957) pioneered a sophisticated physical-numerical analog simulator that was the basis for later 

versions (Appleby and Fujikado, 1960; Estoque, 

1963) adapted to high-speed computer operations. 

Myrup (1969) introduced an analog simulation 

model for urban climate applications that was based 

on the work of Halstead and others (1957). Myrup's 

model was recently modified by Outcalt (1972a) for 

use with a digital computer. The Outcalt version is 

employed in this study to evaluate the physical association between land use and land cover and surface roughness.

The Outcalt numerical model is one version of the 

computer software designed for researching surface 

climate problems. It has evolved from the theoretical 

models previously cited, but it has design characteristics that make it relatively easy to simulate 

changes in surface boundary parameters. The Outcalt model is based on energy conservation principles and uses time-dependent equations to estimate 

energy fluxes in response to a specified set of atmospheric and surface conditions. The solution to the 

energy balance conservation equations is based upon 

equilibrium temperature theory, which allows a 

single surface temperature to be used for convergence with the observed thermal regime (Outcalt, 

1972a). No attempt is made here to discuss the computational details of the model, which are fully described in Myrup (1969) and Outcalt (1972a).

This analysis deals with changes to energy flux 

parameters as a response to differences in surface 

roughness. To activate the numerical simulator, the 

boundary conditions shown in table 21 are specified.

TABLE 21.—Input data (boundary conditions) for the 

numerical simulation experiment

fData for Baltimore, Md., on May 11, 1972]

1. Temporal data:

Latitude

Solar declination

Radius vector of Earth orbit

2. Meteorological data:

Sky radiant temperature

Atmospheric dust content

Mean diurnal air temperature

Wind velocity

Air relative humidity fraction

Precipitable water

Station pressure

3. Geographic data:

Soil volumetric heat capacity 

Soil thermal diffusivity 

Surface albedo fraction 

Surface wet fraction 

Shadow ratio 

Surface roughness

39.9° 

17.7° 

1.010149

-17°C

1 particle cm'3 5°C

100 cm sec"1 

0.40 

10 mm 

1007 mb

0.5 cal cnra "C'1 

0.02 cal cm-1 "C'1 

0.22

0.25 & 0.01 

0.15 

1-2 000 cm

Source: Outcalt, 1972a
C30 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

These conditions were determined from ground observations acquired during a remote sensing experiment flown over Baltimore on May 11, 1972 (Pease 

and others, 1976).

Input data are organized into three categories: 

temporal, meteorological, and geographic. Temporal 

data are a function of latitude and time of year. The 

meteorological data represent average atmospheric 

conditions for the date specified. The geographic 

data are, as hypothesized, functions of land use and 

land cover. Attention is directed specifically to the 

sensitivity of the energy balance to changes in surface roughness. Output from the model consists of 

hourly values of solar radiation components, surface energy transfers, and a soil temperature matrix. The changes to the surface energy balance 

transfers induced by surface roughness are evaluted by the energy conservation equation.

Rn +H + L + S = Q (7)

where Rn is the sum of net radiation, H is the sensible heat flux to the atmosphere, L is the latent heat 

flux, and S is the soil heat flux (table 22). The 

effects of surface roughness on surface radiant temperatures (T) are also evaluated.

SENSITIVITY TESTING

The previous statistical evaluation demonstrates 

that the Baltimore Level III system is the most discriminating land use and land cover system in its 

characterization of surface roughness by land use 

and land cover class. This discrimination is evident 

in the ranking of land use and land cover categories 

for the three systems we compared (fig. 12). The 

USGS Level II system is a conservative generalization of a small number of land use and land cover 

types. Although the Baltimore Zoning Ordinance

TABLE 22.—Output data from numerical simulation model

[All data in millilangleys/min., except T=°C1

Solar radiation components:

Extraterrestrial radiation

Beam radiation

Diffuse hemispherical radiation

Back-scattered reflected hemispherical radiation

Total incoming solar radiation

Surface energy transfers:

Net radiation (Rn ) 

Soil heat flux (S) 

Sensible heat flux (H) 

Latent heat flux (L) 

Surface temperature 

Soil temperature matrix:

Soil temperature at four substrate levels

Source: Outcalt, 1972a

TABLE 23.—Values of the energy balance components 

simulated for Baltimore

[Values for solar noon. Surface wet fraction = 0.25]

(cm)

8

25

50

75

100

125

150

175

200

225

250

275

300

325

350

S

484

421

374

343

311

288

273

252

238

227

212

198

194

182

171

H

( millilangleys/min. )

294

354

398

426

454

474

487

503

513

521

530

538

539

545

549

L

183

207

224

236

249

259

267

277

284

291

301

311

314

324

334

T

(°C)

28.9

25.7

23.3

21.7

20.1

18.9

18.1

17.1

16.4

15.8

15.0

14.3

14.1

13.5

13.0

classification system differentiates a wider range of 

categories, only the high-rise category described by 

zoning classes OR-2, OR-4, and B-45 provides for 

greater discrimination than Level II. The sensitivity 

test described below, therefore, makes use of the 

range of roughness values representative of the Level 

III classification system. In addition, values for the 

CBD, an area possessing unique land use and land 

cover features, are tested separately.

SIMULATION OF BALTIMORE LEVEL III SYSTEM 

VALUES

The simulated surface energy balance components 

for increments of roughness lengths are shown in 

Table 23. The response to increasing amounts of 

surface roughness shows changes in the expected 

direction. As turbulent transfer becomes more pronounced with increased roughening, the latent and 

sensible heat fluxes increase. This increase depletes 

the available surface energy, thereby reducing the 

surface radiant temperature and the soil heat flux 

downward during the mid-day energy regime shown 

in figure 13.

The linear progression of increasing roughness 

lengths clearly shows the rate at which the energy 

flux decreases. The lower roughness values have a 

disproportionate influence on the partitioning of the 

components of net radiation, or available energy. 

For example, roughness lengths up to 125 cm have 

a much greater effect on the sensible heat flux, than 

an equivalent change in the higher roughness values. 

The general trend in the model is a decreasing response in energy transfer to a linear increase in 

surface roughness. This trend matches the log-
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C 31

100 200 300 400 500 600 

ROUGHNESS LENGTH, IN CENTIMETERS

700 800 900

FIGURE 13.—Surface temperature as a function of roughness length.

arithmic form of the wind profile equation in the 

simulation model. This energy balance simulation 

demonstrates that a more accurate estimate of aerodynamic roughness is warranted for land use and 

land cover at the lower range of roughness values.

SIMULATION OF CBD VALUES

Two important parameters influence the energy 

balance components for the unique case of the 

CBD. Exceptionally high surface roughness values 

are generated by the geometry of high-rise building 

clusters. An additional surface parameter that has 

great influence on the energy balance is the evaporation area, which is significantly reduced. Depriving 

the surface of its evaporation opportunity forces 

much more of the available energy into sensible 

heating of the soil and overlying atmosphere. For 

simulation purposes, the surface wet fraction of 0.25 

for urban land use and land cover is changed to 

0.01 to reflect more accurately the hydrologic character of the CBD.

The simulated surface energy balance components 

for the CBD are shown in table 24. The range of 

roughness lengths and the larger incremental steps 

listed in this table, as opposed to those in table 25, 

are representative of estimates derived from the

TABLE 24.—Values of the energy balance components 

simulated for the Baltimore CBD

[Values for solar noon. Surface wet fraction = 0.011

Zo

(cm)

100

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

1500

1600

1700

1800

1900

2000

s

410

322

267

223

190

164

142

124

110

98

87

78

70

63

57

52

47

43

39

36

H

( millilangleys/min. )

570

679

745

798

839

870

884

913

930

943

953

962

969

974

978

981

982

983

983

982

L

13

15

16

18

20

21

23

25

27

30

32

35

38

41

44

48

52

57

62

66

T(°C)

23.6

19.8

17.4

15.5

14.0

12.8

11.9

11.1

10.4

9.9

9.4

8.9

8.6

8.2

8.0

7.7

7.5

7.2

7.1

7.0

evaluation of the unique CBD data set. The latent 

heat component is virtually eliminated from the 

energy partitioning due to the small wet fraction. 

The soil heat flux diminishes rapidly as the rough-
C32 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

ness length increases to exceptionally high values— 

of 500 cm or greater. Beyond this value the estimate 

for the roughness length becomes unrealistically 

large, and a meteorological assessment of such a 

condition is difficult.

The trend of values of the energy balance components for the lower range of roughness lengths 

in table 26, however, is valid with regard to the 

competing physical processes. With the sensible heat 

flux as a measure of the impact of roughness on 

turbulent transfer, the lower range of roughness 

lengths shows the expected disproportionate influence. For example, the effect of a 100-cm increase 

in roughness length on the sensible heat flux at the 

beginning of the test has a greater effect than a 

1,000-cm change at the high end of the roughness 

length scale. The high sensitivity of the values of 

the energy balance components to increments in the 

small values of the roughness length underscores 

the importance of accurately estimating aerodynamic surface properties.

APPLICATIONS OF LAND USE AND LAND

COVER INFORMATION IN URBAN

CLIMATOLOGY

This study has described a methodology for estimating surface roughness by the Lettau model (eq. 

4). Physical similarity theory enables this empirical 

formula to be used to determine surface roughness 

from a description of the wind obstacles. This indirect approach to determining aerodynamic roughness for numerous types of urban land use and land 

cover is presently the only alternative to the difficult 

determination of surface roughness from wind 

profile measurements in areas of complex urban 

buildup.

The statistical analysis of the 324 roughness samples identified by the USGS Level II and the Baltimore Level III land use and land cover systems suggests that reasonable estimates of surface roughness 

can be made at two mesoscale levels. Statistics for 

the Level II and Level III systems are summarized 

in tables 25 and 26, respectively. The USGS Level 

II system can be used to conservatively estimate the 

surface roughness of metropolitan areas from highaltitude photographs. For applications requiring 

greater resolution, Level III land use and land cover 

data may be used. The U.S. Geological Survey has 

not published Level III land use and land cover maps 

but maps can be made by geographic sampling if 

resources are available to the user.

Table 27 lists the roughness lengths specified in 

boundary conditions for recent experiments in

TABLE 25.—Roughness lengths summarized in centimeters 

for USGS Level II land use and land cover classes

Classes *' 

plots

(11) Residential 157 

(12) Commercial 49

& Services

(13) Industrial

(15) Transportation,

Communications,

& Utilities

(16) Institutional

(51-54) Water

(41-43) Forest Land

33

33

12

12

28

Median Mean

Za Zn

88 

107

36

29

62

1

106

100 

181

52

47

130

1

117

8 

5

8

4

14

1

8

Range

to 1022 

to 1468

to

to

to

to

to

229

557

497

2

322

Standard 

deviation

94 

265

51

96

139

0.4

78

TABLE 26.—Roughness lengths summarized in centimeters 

for Baltimore Level HI land use and land cover classes 

and the CBD l

[See table 14 for category descriptions]

Class

OPEN

CLD

ILD

RLD2

WLD

RMD2

RMD2W

CMD

IMD

RMD2W

RMD3

WHD

IHD

RMD4

INST

RMD3W

WHD

HR

CBD

No. 

of 

plots

32

5

39

6

13

43

28

28

15

19

21

13

10

9

12

9

9

16 (')

Median

Zo

8

11

28

39

49

71

73

78

82

86

114

115

126

151

155

185

260

322

673

Mean

Zn

7

11

28

38

48

73

76

87

78

120

113

116

162

147

162

178

245

481

850

Range

1 to 30

7 to 15

8 to 74

29 to 45

30 to 71

30 to 118

38 to 139

10 to 218

39 to 118

51 to 350

43 to 221

60 to 160

35 to 557

89 to 213

20 to 413

96 to 312

185 to 322

128 to 1468

51 to 2118

1 The CBD was sampled as one continuous unit.

urban atmospheric simulation modeling. About half 

of the models cited were assigned a single roughness length to represent the entire city. Sensitivity 

analysis of numerical simulation models of the surface climate has shown, however, that small aerodynamic roughness differences may have significant 

effects on turbulent transfer of energy away from 

the surface-air interface (Myrup, 1969; Nicholas, 

1974). The generalizations in table 27 exist partly 

because of the paucity of actual urban wind profile 

measurements (table 1). Other modeling efforts 

cited in table 27 show some attempt to use a range 

of hypothetical urban roughness lengths, for example, 2 to 300 cm. These stepped values, however, 

are generally limited to use for sensitivity testing
AERODYNAMIC ROUGHNESS RELATED TO LAND USE AND LAND COVER IN BALTIMORE C 33

TABLE 27.—Some recent climatological models using 

roughness lengths in centimeters

Author Model

Bornstein 

(1972)

Davis & Pearson

(1970) 

Atwater

(1972)

Pandolfo & others

(1971) 

Myrup

(1969) 

Outcalt

(1972a)

Miller & others 

(1972)

Myrup & Morgan 

(1972)

Tag

(1969) 

McElroy

(1972)

Chaundry and 

Cermak 

(1971)

A two-layer atmospheric 1 300

model for simulating - 50

nighttime conditions over

a warm, aerodynamically

rough city. 

Physical similarity model of 200

Ft. Wayne, Ind. 

Numerical model of urban ] 100

temperature response to "1

changes in physical surface, pollutant strength 

Numerical model of trans- x 100

port and diffusion 3 1 

Analog model of the urban 1-457

heat island 

Digital model of the urban 2-300

heat island modified from

Myrup (1969) 

A scrutiny of the models of 1 250

Myrup (1969) and Out- -10

calt (1972a) 

Numerical model of urban (")

energy balance at Sacramento, Calif. 

Model of urban surface 1-300

temperature 

Effect of alternative land 5-300

use strategies on the

thermal structure of air

over cities 

Wind tunnel model (*)

1 Urban value 

- Rural value

3 Sample estimates by Lettau's method 

* Not specified

rather than for assigning specific values to particular land use and land cover categories. On the 

other hand, Myrup and Morgan (1972) estimate 

surface roughness for a unique 15-category land 

use and land cover classification system derived 

from 56 urban site samples in Sacramento, Calif. 

This work is the only urban climate model using 

surface roughness estimates derived from an interpretation of land use and land cover classes.

The summaries in tables 26 and 27 provide the 

user with information regarding the principal statistical characteristics of roughness data determined 

in this paper. Other properties of the data may also 

be assessed. Skewness, for example, can be determined in part from the difference between the mean 

and median surface roughness values of a given land 

class. Knowledge of the sample size (in this case, 

the number of plots) also suggests the degree of 

confidence that may be placed in a given estimate.

Knowledge obtained from a given land use and 

land cover map can be applied directly to urban

FIGURE 14.—Contours on surface roughness in Baltimore, 

Md., simulated from USGS Level II land use and land 

cover data. Values are mean roughness lengths, in centimeters.

climate problems by using this information as a 

surrogate for aerodynamic roughness. This application is demonstrated for Baltimore by using land 

use and land cover maps at two scales. Figure 14 

shows the estimated surface roughness derived from 

the USGS Level II land use and land cover map in 

figure 9. This mapped estimate was based on 200 

systematically distributed point samples with data 

assigned to each point from the mean roughness 

values listed in table 26 for each particular land use 

and land cover category. This estimate is conservative because the level of generalization of the Level 

II system is relatively high. Figure 15 shows the 

estimated surface roughness at the Level III scale. 

The planning map in figure 3 was used as a base 

to identify the land use and land cover categories 

at sample locations. Sample values were assigned 

from table 26, and then the distribution was 

mapped. Greatly improved resolution is achieved by 

the Level III estimate, yet the major trends are 

visible in the Level II estimate in figure 14.
C34 THE INFLUENCES OF LAND USE AND LAND COVER IN CLIMATE ANALYSIS

FIGURE 15.—Contours on surface roughness in Baltimore, 

Md., simulated from Baltimore Level III land use and 

land cover da ba. Values are mean roughness lengths, in 

centimeters.

This method of relating land use and land cover 

characteristics to aerodynamic roughness shows 

that acquiring indirect estimates of environmental 

parameters from sources such as high-altitude 

photographs can provide a method for rapidly producing a reliable spatial distribution of these parameters. Although subject to verification by careful ground measurements, the use of surrogates 

such as descriptions of land use and land cover for 

the measurement of environmental parameters can 

improve the accuracy of the specification of those 

boundary conditions important to climatic simulation modeling and ultimately to urban growth.

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