In cooperation with Oakland County, Michigan

Effects of Urban Land-Use Change on Streamflow and Water 

Quality in Oakland County, Michigan, 1970-2003, as Inferred 

from Urban Gradient and Temporal Analysis

Scientific Investigations Report 2005-5016

U.S. Department of the Interior

U.S. Geological Survey
Effects of Urban Land-Use Change on 

Streamflow and Water Quality in Oakland 

County, Michigan, 1970-2003, as Inferred 

from Urban Gradient and Temporal Analysis 

By Stephen S. Aichele

In cooperation with Oakland County, Michigan

Scientific Investigations Report 2005-5016

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat, Director

U.S. Geological Survey, Reston, Virginia: 2005

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Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Aichele, Stephen, 2005, Effects of Urban Land-Use Change on Streamflow and Water Quality in Oakland County, 

Michigan, 1970-2003, as Inferred from Urban Gradient and Temporal Analysis: U.S. Geological Survey Scientific 

Investigations Report 2005-5016, 22p.
iii

Contents

Abstract ................................................................................................... 1

Introduction ............................................................................................. 1

Purpose and Scope .............................................................................. 2

Anthropogenic Influences on Hydrologic Systems ....................................... 2

Methods ................................................................................................... 4

Land-cover and Population Analysis ......................................................... 4

Land-cover-data standardization ...................................................... 4

Population-Data Standardization ...................................................... 7

Hydrologic Data ................................................................................. 8

Streamflow Data ........................................................................... 8

Discrete Water-Quality Data ............................................................ 10

Streamflow Characteristics ..................................................................... 11

Discrete Water-Quality Comparison ......................................................... 12

Correlations Among Watersheds Across the Urban Gradient ................................. 12

Changes in Land Cover and Population Density .......................................... 12

Correlation Between Land Cover and Streamflow Across the Urban Gradient ... 16

Correlation Between Land Cover and Water Quality Across the Urban 

Gradient ............................................................................................. 16

Changes in Streamflow and Water Quality Through Time .................................... 16

Changes in Streamflow Characteristics ...................................................... 17

Changes in Stream-Water Quality Through Time ....................................... 18

Summary and Conclusions ........................................................................... 20

Acknowledgments ....................................................................................... 21

References Cited ....................................................................................... 21

List of Figures

 1. Conversion of land to urban covers in Oakland County, Mich, between 

 1980 and 2000 ................................................................................. 3

 2. Maps showing land-use/land-cover change in Oakland County, Mich, 

 1980-2000 .................................................................................... 5

 3. Graph showing relative landscape representation for selected land covers at 

 various cell sizes .............................................................................. 6

 4. Locations of discrete water-quality sample sites, continuous streamflow-

 gaging stations, and specific conductance monitors ................................. 9

 5. Comparison of geometric mean (a.) specific conductance, (b.) chloride 

 concentration, (c.)phosphorous concentration, (d.) sulfate concentration, 

 (e.) nitrate concentration, and (f.) noncarbonated hardness of discrete 

 water-quality samples collected during low flow conditions, 1966-1970 and 

 2001-2003 .......................................................................................... 19
iv

List of Tables

 1. Relative percent landscape representation for selected land covers at 

 various cell sizes .............................................................................. 6

 2. Fitted population-density coefficients by year for each residential 

 land-use/land-cover class in southeast Michigan .................................... 8

 3. Identification and period of record for streamflow-gaging stations used 

 in this study ................................................................................. 9

 4. Sites where discrete water-quality samples were collected during 

 2001–2003 .................................................................................... 10

 5. Water-quality characteristics and analytical methods for inorganic 

 stream-water-quality samples collected in Oakland County, Michigan, 

 during 2001, 2002, and 2003 ............................................................... 11

 6. Land-use/land-cover composition of monitored watersheds in Oakland 

 County, Michigan, 1980 to 2000 ......................................................... 12

 7. Estimated population densities in gaged watersheds in Oakland County, 

 Michigan, 1980 to 2000. ..................................................................... 14

 8. Spearman Rho values correlating specific characteristics of daily 

 streamflow to land-cover and urbanization metrics across an urban 

 gradient ....................................................................................... 16

 9. Spearman rank order correlations between water-quality parameters and 

 land-use/land-cover metrics ............................................................... 17

 10. Spearman rank-order correlations between year and selected annual 

 daily-streamflow statistics for the water years 1970 through 2003 ............... 18

Conversion Factors and Datum

Inch/Pound to SI

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

Area

acre 4,047 square meter (m2)

acre 0.004047 square kilometer (km2)

square mile (sq. mi.) 2.590 square kilometer (km2) 

Flow rate

million gallons per day (Mgal/d) 3,785 cubic meter per day (m3/d)

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Water year, as used in this report, refers to the period from October 1 to September 30. For example, 

water year 2004 begins October 1, 2003, and ends September 30, 2004.
Effects of urban land-use change on streamflow and 

water quality in Oakland County, Michigan, 1970-2003, as 

inferred from urban gradient and temporal analysis

By Stephen S. Aichele

Abstract

Various adverse hydrologic effects on streams have 

been attributed to urban development and expanded 

impervious surface area, including increased high 

flows, decreased low flows, increased variability (commonly referred to as flashiness), nutrient enrichment, 

and increased dissolved solids concentrations. These 

effects are often observed through the use of urban-gradient studies, which compare hydrologic characteristics 

among watersheds with different levels of development. 

This technique is frequently applied when comparable 

prior data are not available for the watersheds of interest. 

During 1966 - 1970, and again during 2001 - 2003, 

the U.S. Geological Survey collected a series of lowflow water-chemistry samples. Streamflow-gaging 

stations were operated throughout the period from 1966-

2003 as part of ongoing monitoring operations. This 

study compares these two water-quality data sets; tests 

the streamflow data for trends in high flows, low flows, 

and flashiness; and correlates 2000 land use with waterquality and streamflow data collected during the 2001 

- 2003 study.

Despite substantial change in land use during 1980 

- 2000, with urban land covers replacing open space, 

forest, and agriculture, little evidence is found in the 

time-series data of alteration of the daily streamflow 

characteristics or nutrient enrichment in the study watersheds. However, a distinct shift is observable in chloride 

concentrations. Strong positive correlations exist across 

the urban gradient between development and increased 

peak flows as well as between development and 

increased flashiness. Correlations of water-quality data 

to development metrics show strong positive correlations 

with increased dissolved solids and salt content, as well

as increased concentrations of fecal indicator bacteria 

(Eschericia coli).

This apparent contradiction may be caused by the 

differences in the changes measured in each analysis. 

The change-through-time approach describes change 

from a fixed starting point of approximately 1970; the 

gradient approach describes the cumulative effect of 

all change up to approximately 2000. These findings 

indicate that although urbanization in Oakland County 

results in most of the effects observed in the literature, 

as evidenced in the gradient approach, relatively few of 

the anticipated effects have been observed during the 

past three decades. This relative stability despite rapid 

land-cover change may be related to efforts to mitigate 

the effects of development and a general decrease in the 

density of new residential development. It may also be 

related to external factors such as climate variability and 

reduced atmospheric deposition of specific chemicals. 

Introduction

Land-cover change — and more specifically the 

rate of land-cover change — has been identified as a 

serious issue in Michigan (Michigan Land Use Leadership Council, 2003) and elsewhere in the United States. 

Societal changes, such as those in the transportation 

infrastructure and the economy, have made the centralization of services and activities associated with cities 

less necessary, resulting in more dispersed settlement 

(Garreau, 1992). The postulated effects of land-cover 

change are widespread, ranging from destruction of habitat (Malmqvist and Rundle, 2002) to alteration of climate 

(Kalnai and Cal, 2003). In addition, land-cover change 

has been clearly documented to have a significant
2 Effects of urban land-use change on streamflow and water quality in Oakland County, Michigan, 1970-2003

influence on hydrologic systems (Leopold, 1968).

These effects are of particular interest in areas 

undergoing rapid urban expansion, such as Oakland 

County, Michigan, which includes 910 square miles 

within the Detroit metropolitan area (figure 1). Oakland 

County also provides a rare opportunity to study these 

effects over a long term, because a comprehensive water 

resources assessment was completed in 1972 (Twenter 

and Knutilla, 1972), and because of the large number of 

continuously operated streamflow-gaging stations in the 

county.

Purpose and Scope

This report describes the streamflow and waterquality effects of land-use change in 14 watersheds in 

Oakland County, Mich., by making comparisons to 

historical data and by correlating hydrologic characteristics to land use across an urban gradient. Urban gradient 

studies compare hydrologic characteristics among watersheds with different levels of development, a technique 

frequently applied when comparable prior data are not 

available for the watersheds of interest. A process is 

described to standardize disparate land-cover source 

datasets to consistent measures of land cover and population density in watersheds through time. The resulting 

standardized land-cover data are then compared to several metrics of streamflow and water quality intended to 

describe both change through time in specific watersheds 

and concurrent effects across watersheds. These metrics 

include the 1- and 99-percent-exceedance flows, streamflow variability, and discretely measured water-quality 

characteristics.

Anthropogenic Influences on Hydrologic 

Systems

The effects of urbanization on hydrologic systems 

have been a concern in America for more than 200 

years. Benjamin Franklin, writing in his will, expressed 

concern that the tendency to cover land with buildings 

and pavements would have detrimental impacts on the 

quantity and quality of water available in the wells then 

serving the City of Philadelphia (Smyth, 1907). Today 

our concerns are much the same, only projected from 

within city boundaries to the outlying suburban areas.

In response to the development boom during the 

period following World War II, numerous authors 

addressed the issue of urbanization in the 1960s. A 

series of USGS Water-Supply Papers (WSP 1591A - E) 

described the then-current state of the science regarding influences of urbanization and land-cover change 

on water supply, water quality, flood inundation, and 

sediment transport, as well as difficulties in modeling the 

engineered urban hydrology systems. This group of 

studies all relied on temporal analysis methods to evaluate hydrologic trends in specific watersheds.

Savini and Kammerer (1961) published an overview of interactions between urbanization and water 

resources. The focus was on providing safe drinkingwater supplies for communities and sufficient water 

flows to meet other uses, such as irrigation and waste 

disposal, without impairing water quality. A study of 

Permanente Creek in California (Harris and Rantz, 1964) 

yielded clear evidence of increased runoff as a result of 

urbanization through the analysis of streamflow records 

and aerial photography. An increase in impervious 

surface from 4 percent to 19 percent of watershed area 

resulted in a 44-percent increase in peak flows. Studies 

of flood-frequency characteristics in seven basins surrounding Charlotte, N. C. (Martens, 1968) documented 

the increased flood potential associated with increased 

impervious surfaces. Finally, study of streamflow and 

sediment transport in a developing watershed in northern Virginia (Vice and others, 1969) identified sediment 

loads between 10 and 2,000 times greater than with 

undeveloped land covers.

In 1968, Luna Leopold summarized much of this 

research in his “Hydrology for Urban Planning - A 

Guidebook on the Hydrologic Effects of Urban Land 

Use” (Leopold, 1968). These foundational studies, however, focus largely on the development model employed 

from 1945-1970 – relatively small properties with large 

fractions of the property in impervious surface, as well 

as curbs, sidewalks, storm drains, and sewers. Runoff 

control structures, now required, were rare, as were the 

larger-lot rural residences that have become increasingly 

common in recent decades.

Subsequent research in this area has largely added 

supplemental evidence and detail to the processes proposed in these studies. Owe (1985) identified alteration in the long-term water budget of the Chester Creek 

watershed in Pennsylvania as a result of suburban development by looking at cumulative development metrics, 

as opposed to change. McMahon and others (2003), 

who studied more recently developed areas, suggested 

that the effects of urbanization may be ameliorated to 

some degree by fragmentation of developed areas. In 

other words, one large developed area may have a larger 

effect than many small developed areas with the same 

total area. This finding may indicate a shift in the way 

land cover relates to hydrology, based on change in landdevelopment patterns and practices. Choi and others 

(2003) identified a relation between the annual rainfall 

and the flow volumes in urbanizing watersheds.

The effects of urbanization on water chemistry 

are not as certain. Tufford and others (2003) observed 

higher dissolved organic nitrogen and ammonia concentrations in forested watersheds than in urban watersheds, 

whereas nitrate and total phosphorus were higher in 

urban watersheds. However, Tufford and others (2003)
were unable to develop significant statistical relations 

between land cover and nutrient concentrations in their 

study streams. Im and others (2003) evaluated various 

development scenarios in the Polecat Creek watershed of 

Virginia. This study indicated that although runoff volumes would increase, phosphorus and nitrogen concentrations would generally decrease because of decreased 

agricultural land. Nitrate, specifically, is projected to 

increase because of use of lawn fertilizers.

Several recent studies have investigated the effects of 

urbanization in fecal indicator bacteria. Smith and others 

(2001) identified strong correlations between urban land 

covers and water-quality impairment as indicated by fecal 

coliforms. Frenzel and Couvillion (2002) have found 

strong correlations between urbanization and concentrations of fecal indicator bacteria in surface water. The 

same research indicates that significantly higher concentrations of fecal indicator bacteria were present in watersheds with sanitary sewers than those served primarily by 

septic systems, although this difference may be caused by 

the presence of storm sewers in addition to sanitary sewers in these more urbanized watersheds.

Most of these later studies can be characterized as 

gradient studies (McMahon and Cuffney, 2000); that is, 

they use data collected over short time periods from several 

geologically similar watersheds with varying degrees of 

human influences and evaluate effect based on correlation analysis. In essence, these studies substitute change 

through space for change through time. These techniques 

are useful in specific applications and allow the use of data 

collected with a standard set of methods specifically for the 

study. However, these gradient methods are not particularly sensitive to changes across fixed-time intervals.

Both the cumulative and gradient approaches make 

an implicit assumption that the hydrologic systems have 

equilibrated to changes in the watershed. This assumption 

is difficult to verify, and may in fact be technically invalid 

in many instances, because the land-cover change within 

the watershed is ongoing. However, the watersheds discussed herein are relatively small and would be expected to 

respond quickly to change. This study employs both techniques to assess the cumulative effect of land-use change in 

Oakland County (fig. 1), as well as the incremental effect 

of change since the late 1960s.

Figure 1. Conversion of land to urban cover in Oakland County, Mich, between 1980 and 2000.

83°30’

83°15’

42°45’

42°30’

Political boundaries from Oakland County GIS Utility, 2003.

Hydrographic data from USGS 1:100,000 scale Digital Line Graphs.

Land cover data from Southeast Michigan Council of Governments.

Oakland

County

Michigan

Flint River

Watershed

Shiawassee River

Watershed

Huron River

Watershed

River Rouge

Watershed

Clinton River

Watershed

8 KILOMETERS

MILES

2 4

0 2 4 8

0

EXPLANATION

Land cover change to urban

Municipal boundary

Watershed boundary

Holly Groveland Brandon Oxford Addison

Rose Springfield Independence Orion Oakland

Highland White Lake Waterford Pontiac Avon

Milford Commerce

West

Bloomfield Bloomfield Troy

Lyon Novi Farmington Southfield Royal Oak

Anthropogenic Influences on Hydrologic Systems 3 
4 Effects of urban land-use change on streamflow and water quality in Oakland County, Michigan, 1970-2003

Methods

In order to quantify the effects of land-cover and 

population change on water resources, the data used in 

the analysis must first be standardized across both the 

period of study and the area of study. These standardized datasets can then be used for analysis. The next 

several sections describe the methods applied to the five 

land-cover datasets, three population data sets, continuous stream flow, and discrete water-quality samples.

Land-cover and Population Analysis 

Land-cover data for the years 1985, 1990, 1995, and 

2000 were obtained from Southeast Michigan Council of 

Governments (SEMCOG; Lisa Mayoras, written commun., 2003). Land-cover data for the year 1980 were 

obtained from the Michigan Geographic Data Library 

(Michigan Center for Geographic Information, 2002). 

Population data for the period from 1950 through 2000 

were obtained from SEMCOG (Ann Burns, written 

commun., 2003). Standardization of both the spatial and 

attribute content of the datasets was a primary concern in 

ensuring that land-cover changes identified were indeed 

true changes, not the result of positional or classification 

differences between datasets.

Classification errors result from differences in attribution standards, either among photo interpreters in a 

single time period or between different mapping efforts. 

The more specific the classification, the more subjective 

error will be introduced. For instance, a light-industrial 

parcel could easily be confused for a commercial parcel 

based on aerial photography, but both would unquestionably be classified as “urban.” In a wet year, a lowland 

forest may be classified as a wooded wetland. Although 

all data were developed by visual classification of aerial 

imagery, differences in standards, classification schemes, 

and even software result in small differences between 

data sets. The datasets used in this project were created 

at five different times over a period of approximately 20 

years by countless individual photointerpreters. Consistency within one mapping effort might be possible, 

but complete consistency through the entire data series 

is unlikely. Further, the classification system itself 

changed slightly through time, resulting in slight inconsistency.

Spatial errors are characterized by the assignment of 

different boundaries to a feature with the same attribute 

content in two datasets. These errors result from differences in registration standards, changes in software or 

processing standards, and differences in the real or interpreted position of a class boundary. For example, the 

several interpreters may draw the boundary of a wetland 

or farmstead differently despite using the same source 

image. Frequently these types of errors are manifest

as “slivers” – small areas where the boundaries of 

mapped areas do not coincide. Although individual 

slivers may cancel each other out, a systematic misregistration between datasets can result in more change being 

detected than has actually occurred. Methods were 

developed to collapse hydrologically similar classifications into larger groups and to identify a rasterization 

scale that would minimize positional errors by generalizing boundaries while maintaining the integrity of the 

source data.

Land-cover-data standardization

Land-cover data were collected by SEMCOG for 

five discrete time periods. Although all data were created by visual classification of aerial imagery, differences in standards, classification schemes, and even 

software have resulted in small differences between data 

sets. The datasets were standardized for both positional 

and classification accuracy by reclassification and rasterization, respectively.

Land-cover data were reclassified to provide attribute accuracy to address specific questions. The first 

set of questions relate generally to the effect of landcover change on hydrology. The resulting map, figure 2, 

shows residential, commercial, and other built features, 

as well as agriculture, open land, forest, wetland, and 

open water. The land-cover data were also reclassified 

to differentiate densities of residential space for subsequent distribution of population. In this case, only 

residential space was identified, including high-rise 

multifamily, low-rise multifamily, detached single-family or duplex, mobile home parks, and farmsteads. All 

other space was classified as non-residential. Thus, for 

each time period, two land-cover maps were generated, 

one showing overall land cover at a level of detail similar to a one-digit Anderson classification (Anderson and 

others, 1976) and the second showing only residential 

space, classified to a level of detail similar to a threedigit Anderson classification.

To address small inconsistencies in the spatial registration of the dataset, as well as to facilitate processing, the original vector land-cover data were converted 

to raster (gridded) data. Selection of an appropriate 

cell size was based on balancing two conflicting goals: 

although larger cell sizes minimize the effect of small 

spatial registration errors, larger cell sizes also lead to 

over-representation of dominant cover types (Turner and 

others, 1989). Therefore, a series of grids were generated with cell sizes ranging from 1 m to 300 m, and the 

relative representation of each land-cover class at each 

cell size was tabulated and graphed. An arbitrary threshold was set specifiying the largest cell size that resulted 

in less than 1 percent change in representation for any 

cover type in either the general land-cover or population
Figure 2. Land-use/land-cover change in Oakland County, Mich, between 1980 to 2000.

0

50

100

150

200

250

300

350

400

1980 1985 1990 1995 2000

AREA, IN SQU

ARE MILES

YEAR

Commercial

Residential

Michigan

Oakland

County

Residential

Commercial or Transportation

Agriculture

Open land

Forest

Water

Wetland

Barren

Municipal boundaries

Land use/land cover

Scale 1:700,000

Explanation

Land use/land cover, 1990 Land converted to urban use, 1980 to 2000

Land use/land cover, 1980 Land use/land cover, 2000

R. 7E R. 8E R. 9E R. 10E R. 11E

T. 5N

T. 4N

T. 3N

T. 2N

T. 1N

Land-cover-data standardization 5
6 Effects of urban land-use change on streamflow and water quality in Oakland County, Michigan, 1970-2003

classifications. Figure 3 and table 1 show the change in 

cover representation for the general land-cover classification for Oakland County as a whole. In this case, a

60-m cell size was selected. On the basis of the same 

criteria, population data were rasterized at a 20-m cell 

size.

Figure 3. Relative landscape representation for selected land covers at various cell sizes.

CELL SIZE, IN METERS

PE

R

C

E

N

T

R

EPR

ESE

N

T

A

TIO

N

EXPLANATION

General Urban

Residential

Commercial

Agriculture

Water

Wetland

0 30 60 90 120 150 180 210 240 270 300

115

110

105

100

95

90

85

80

Table 1. Relative percent landscape representation for selected land covers at various cell sizes.

[Cell sizes in meters on a side; all values in percent]

Cell size 

Land-cover type 5 10 20 30 60 90 100 150 200 300

Undifferentiated urban 100 100 100 100 100 100 99 97 95 92

Residential 100 100 100 100 100 101 101 103 105 110

Commercial 100 100 100 100 100 100 100 99 97 94

Agricultural 100 100 100 100 100 100 101 102 105 107

Open space 100 100 100 100 100 100 100 99 99 96

Forest 100 100 100 100 100 99 99 97 94 87

Water 100 100 100 100 99 98 98 96 94 90

Wetland 100 100 100 100 100 99 99 96 92 86

Barren 100 99 101 92 101 75 124 70 124 0
Population-Data Standardization

In order to accurately describe the effect of population density on water resources, it was first necessary to 

develop population densities based on watershed boundaries. However, population data are seldom, if ever, generated with reference to watershed boundaries. Population data are usually based on a set of nested polygons 

describing blocks, tracts, block groups, precincts, minor 

civil divisions (MCDs), counties, or other nonhydrologic 

units. Although tempting to simply overlay a watershed 

on these units and assign area-weighted densities from 

the census units to the watershed, such an approach 

makes the implicit assumption that population is distributed evenly across the census unit, which is frequently 

not the case.

This problem of the uneven distribution of a property within an arbitrary area has been identified as the 

modifiable areal unit problem, or MAUP, by Openshaw 

(1983) and several subsequent authors. The MAUP, 

stated simply, is that the summary characteristics of an 

area change as the boundaries of the area change. For 

example, redrawing a census boundary, even if the new 

polygon has the same area, will likely result in a different population value because the property of population 

is not uniformly distributed. Thus, altering the boundaries of the original census polygon, as in an overlay, alters 

the data values associated with the polygon. Rather than 

split the polygon, one must disaggregate the summary 

information within the polygon, then reaggregate it to a 

new set of boundaries. One approach commonly used is 

dasymetric mapping.

Dasymetric mapping uses ancillary information 

to distribute the characteristic of interest, in this case 

population, within the original census polygon. The 

technique was first described by Wright (1936) where 

population was distributed within municipalities on 

Cape Cod on the basis of road density. Monmonier and 

Schnell (1984) approached the problem by distinguishing residential land from other land-cover types on the 

basis of 1:250,000 scale land-cover data. Eicher and 

Brewer (2001) experimented with a set of approaches 

that assigned relative densities to land-cover classes 

within the civil mapping units. More recently, Mennis 

(2003) demonstrated a technique that samples population 

density from census polygons with uniform land cover to 

assign a density to the land cover, then applies that population density for all instances of that land cover. The 

advantage of this last technique is that more land-cover 

types can be used to distribute population. Two key disadvantages are the need for at least one population mapping unit solely comprised of a single land cover, and the 

assumption that there is no trend in density within individual land-cover classes across the study area. Within 

the study area, entire census polygons with uniform land 

cover were not available for most residential land covers.

For this study, the methods of Monmonier and Schnell (1984) and Mennis (2003) were combined. First, the 

land cover data for 1980, 1990, and 2000 were separated 

into residential and nonresidential groups. Nonresidential land, such as forest, industrial, and water are assumed 

to have no population density. Five residential land 

covers were identified and tabulated for 234 MCDs in 

southeast Michigan. A first estimate, based on Monmonier and Schnell (1984), would be to simply divide the 

population in the MCD by the residential area to arrive at 

a residential population density for the MCD. This value 

generally reflects differences in density across the urban 

gradient but does not utilize the full range of data available. Rather, relative weight coefficients were obtained 

by solving a series of simultaneous equations.

The dominant residential land cover by area in every 

MCD in each of the three time periods with population 

data is detached single family, comprising between 53 

and 100 percent of the residential land cover. This residential land cover is the norm and is assigned a coefficient of 1. When the coefficient is multiplied by the average residential population density for an MCD (described 

above), the result is the actual residential population 

density for the MCD. This simultaneously sets a “typical” density based on the most abundant housing classification and furnishes an overall density adjustment to 

account for differences along the urban gradient. A set of 

simultaneous equations was developed to assign a relative density to the other four classes, with the constraints 

that multifamily high-rise residential must have a greater 

density than multifamily low-rise residential, which in 

turn must have a greater density than detached singlefamily dwellings or mobile home park. Farmstead was 

specified as the lowest density residential cover.

The assumption is made that the relative change 

in density between land-cover classes was constant. 

Expressed slightly differently, although the absolute 

density (expressed as people per unit area) of multifamily low-rise housing, for example, is undoubtedly higher 

in the urban cores compared to rural areas, the relative 

density of multifamily low-rise housing compared to 

single-family, detached housing is assumed to be roughly 

constant. This assumption is based on the market principle that the factors driving housing density — the supply 

of land and the demand for housing — operate similarly 

across the region. 

By solving this set of simultaneous equations, multifamily high rise was found to have a population density 

3.6 times greater than detached single family, multifamily low rise was found to have a density 1.6 times greater 

than single-family detached, mobile home parks were 

found to have a density 1.1 times as great as single-family detached, and farmsteads were found to have a density 

0.50 times as great as single-family detached in the year 

2000. Coefficients for each residential land cover in each 

timeframe are in table 2.

Population-Data Standardization 7
8 Effects of urban land-use change on streamflow and water quality in Oakland County, Michigan, 1970-2003

Table 2. Fitted population-density coefficients by year for each residential land-use/land-cover class in southeast Michigan.

[Note: these values must be multiplied by the average residential population density in each municipality to arrive at a true density]

Density Coefficient

Land-use/land-cover class 1980 1990 2000

High-rise, multifamily residential 13.40 12.00 3.64

Low-rise, multifamily residential 1.72 1.55 1.64

Detached, single family 1.00 1.00 1.00

Mobile home park 0.72 0.68 1.00

Farmstead 0.27 0.59 0.50

The resulting density coefficients were multiplied by the area of each land-cover class within each 

MCD and new population totals. For example, Orion 

Township contained 0 cells of high-rise, multifamily 

(HRMF); 1,063 cells of low-rise, multifamily(LRMF); 

42,037 cells of detached single-family/duplex (DSFD); 

0 cells of mobile home parks (MHP); and 71 cells of 

farmsteads (FS). The overall residential density was 

0.705 person per cell. Each cell is 400 m2. Thus, to 

calculate the estimated population, one would multiply 

each land cover by the overall density and the coefficient from table 2.

Cover Area 

(cells)

Overall density 

(persons per cell)

Coefficient Product 

(persons)

HRMF 0 x .705 x 3.6 = 0

LRMF 1,063 x .705 x 1.7 = 1,300

DSFD 42,037 x .705 x 1.0 = 30,000

MHP 0 x .705 x 1.0 = 0 

FS 71 x .705 x 0.5 = 25

Sum 31,325

This result is 1.8 percent higher than the population 

of exactly 30,748 reported by SEMCOG, although some 

of that difference is the result of rounding to include 

only significant figures.

The intent of the exercise is not to model characteristics but rather to develop a method of distributing population across a mapping unit. Comparing the distributed population to the composite MCD level estimates 

produces an unbiased fit with a correlation of 0.9995. 

This approach does not fully meet Tobler’s (1979) 

requirement that such a mapping be pycnophylactic; 

that is, that people are neither created nor destroyed in 

the redistribution process. Throughout the seven-county 

region, approximately 130,000 people were “created,” 

out of a total population of 4.8 million. The majority 

of these errors were in Detroit and in several of the very 

rural MCD’s, which represent the highest and lowest 

densities in the study area, respectively. This overall 

error level is appropriate to the analysis needs of this 

study.

The result is a raster map of population density 

expressed as persons per cell, in this case a 20-m by 20-

m grid cell. This density map can then be overlain with 

the watershed boundaries to arrive at population density 

characteristics for each watershed area.

Hydrologic Data

Three major types of hydrologic data are discussed 

in this report; continuously measured streamflow, continuously measured specific conductance, and discretely 

measured water-quality data from 1966–1970 and 

2001–2003. Locations where streamflow or specific 

conductance was monitored, or samples were collected, 

are shown in figure 4. Data collected during the 2001–

2003 collection effort can be found in Aichele and others 

(2004). Data collected during the 1966–1970 sampling 

efforts can be found in Michigan District annual data 

reports from the appropriate year, as well as the USGS 

National Water Information System database (NWIS) 

(U.S. Geological Survey, 2003).

Streamflow Data

Continuous daily streamflow data for the period 

1970 through 2003 were obtained from the USGS NWIS 

database. Although most streamflow-gaging stations 

were operated continuously throughout the period, several stations were discontinued for one or more years. A 

complete listing of station identifiers, descriptions, and 

years operated is given in table 3.
Figure 4. Locations of discrete water-quality sample sites, continuous streamflow-gaging stations, and specific conductance monitors.

42° 30’

42°45’

83°45’ 83°30’ 83°15’ 83°0’

04143900

04143830

04148035

04160800

04160900

04170000

04170500

04161580

04161000

04161540

04161810

04166000

04166315

04166100

04166200

04166300

Political boundaries from Oakland County GIS Utility, 2003.

Hydrographic data from USGS 1:100,000 scale Digital Line Graphs.

Oakland

County

Michigan

0 3 6 KILOMETERS

0 3 6 MILES

Watershed boundary

Municipal boundary

Stream water-quality sample locations

Streamflow-monitoring station

EXPLANATION

Flint River

Watershed

Shiawassee River

Watershed

Huron River

Watershed River Rouge

Watershed

Clinton River

Watershed

Table 3. Identification and period of record for streamflow-gaging stations used in this study.

[Refer to figure 4 for station locations]

* Station 04161000 was operated as a crest-stage gage from 1982 to 1991 and throughout water year 2003.

Station number Station name Year constructed Years inactive Drainage area 

(square miles)

04143900 Shiawassee River at Linden, Mich. 1967 1994–2001 49.2

04148140 Kearsley Creek near Davison, Mich. 1965 -- 19.6

04160800 Sashabaw Creek near Drayton Plains, Mich. 1959 -- 20.9

04160900 Clinton River near Drayton Plains, Mich. 1959 -- 79.2

04161000 Clinton River at Auburn Hills, Mich. 1939 1940–1956, 1982–2001* 123

04161540 Paint Creek at Rochester, Mich. 1959 -- 70.9

04161580 Stony Creek near Romeo, Mich. 1964 -- 25.6

04164000 Clinton River near Fraser, Mich. 1947 -- 299

04166000 River Rouge at Birmingham, Mich. 1950 -- 33.3

04166100 River Rouge at Southfield, Mich. 1958 -- 87.9

04166200 Evans Ditch at Southfield, Mich. 1958 -- 9.49

04166300 Upper River Rouge at Farmington, Mich. 1958 -- 17.5

04170000 Huron River at Milford, Mich. 1948 -- 132

04170500 Huron River near New Hudson, Mich. 1948 -- 148

Streamflow Data 9
10 Effects of urban land-use change on streamflow and water quality in Oakland County, Michigan, 1970-2003

Discrete Water-Quality Data

Water-quality results for 19 sites in Oakland 

County are provided in USGS Water-Supply Paper 2000 

(Twenter and Knutilla, 1972). Nine of these sites were 

sampled once only in 1966, and ten were sampled twice. 

No site was sampled more than twice. The characteristics measured included temperature, pH, specific 

conductance, bicarbonate (HCO3), carbonate (CO3), 

sulfate (SO4), chloride (Cl), fluoride (F), nitrate (NO3), 

phosphate (PO4), dissolved solids, total hardness, and 

noncarbonate hardness. Low-flow water-quality samples 

were also collected from specific sites and analyzed for 

specific subsets of these characteristics during water 

years 1967-1970.

For comparison, 14 sites were sampled between 4 

and 18 times during September 2001 through September 2003 under varying flow conditions. At each site, 

several samples were collected under spring and summer low-flow conditions to parallel the sample collection conditions of the 1966–1970 period. Low flow for 

these relatively small watersheds was defined as three 

or more consecutive days of declining discharge and 

instantaneous discharge below the 75th percentile of 

annual daily discharge. Twelve of the 14 sites were also 

sampled in 1966 (Twenter and Knutilla, 1972). One site, 

the Upper River Rouge near Clarenceville (04166315), 

was substituted for the Upper River Rouge at Farmington (04166300). A second site, Kearsley Creek at 

Mill Street at Ortonville, Mich. (04148035), was substituted for the site Kearsley Creek at Ortonville, Mich. 

(04148020). Both substitutions were slightly downstream from the previous study sites and were made 

because of concerns with access to the sites. Neither 

change involved the addition of significant tributaries. A 

complete list of the sites sampled in 2001 through 2003 

is given in table 4.

All samples were collected in accordance with standard protocols developed for the USGS National WaterQuality Assessment (NAWQA) as described in Shelton 

(1994). Eschericia coli (E. coli) were quantified at all 

sites on several occasions in accordance with the methods described in Myers and Sylvester (1997). Temperature, specific conductance, pH, and dissolved oxygen 

were measured in the field with a multiparameter meter. 

Alkalinity and bicarbonate were determined in the field 

by titration. All other chemical analyses were done at 

the USGS National Water Quality Laboratory in Denver, 

Colo. A list of the characteristics measured, the methods 

used, and minimum reporting level (MRL) is given in 

Table 5. Detailed analytical results of the sample-collection and analytical methods are included in Aichele and 

others (2004). 

Table 4. Sites where discrete water-quality samples were collected during 2001–2003. 

[Sites marked with asterisks are also streamflow-gaging stations; refer to figure 4 for station locations]

Site number Site name Drainage area 

(square miles)

04143830 Shiawassee River near Holly, Mich. 49.2

04148035 Kearsley Creek at Mill Street at Ortonville, Mich. 20.1

04160800 Sashabaw Creek near Drayton Plains, Mich.* 20.9

04160900 Clinton River near Drayton Plains, Mich.* 79.2

04161000 Clinton River at Auburn Hills, Mich.* 123.0

04161540 Paint Creek at Rochester, Mich.* 70.9

04161580 Stony Creek near Romeo, Mich.* 25.6

04161810 Clinton River at Yates, Mich. 299.0

04166100 River Rouge at Southfield, Mich.* 87.9

04166200 Evans Ditch at Southfield, Mich.* 9.5

04166315 Upper River Rouge at Clarenceville, Mich. 19.8

04170000 Huron River at Milford, Mich.* 132.0

04170500 Huron River near New Hudson, Mich.* 148.0
Table 5. Water-quality characteristics and analytical methods for inorganic stream-water-quality samples collected in Oakland County, 

Michigan, during 2001, 2002, and 2003.

[MRL, Minimum Reporting Level; mg/L, milligrams per liter; oC, degrees Celsius; μg/L, micrograms per liter; μS/cm, microsiemens per centimeter at 25

degrees Celsius]

Parameter 

code

Characteristic Units MRL Method Reference

00095 Specific conductance, field μS/cm 1.0 Wilde and Radtke, 1998

00400 pH, field Standard units 0.1 Wilde and Radtke, 1998

00403 pH, laboratory Standard units .1 I258785 Fishman and Friedman, 1989

00453 Bicarbonate, field mg/L as CaCO3 1.0 Wilde and Radtke, 1998

00530 Residue, total mg/L 1.0 I376585 Fishman and Friedman, 1989

00608 Nitrogen, ammonia mg/L as N .02 I252290 Fishman, 1993

00613 Nitrogen, nitrite mg/L as N .01 I254090 Fishman, 1993

00623 Nitrogen, ammonia & organic mg/L as N .1 I261091 Patton and Truitt, 1992

00625 Nitrogen, ammonia & organic mg/L as N .1 I451591 Patton and Truitt, 2000

00631 NO2 + NO3, dissolved mg/L as N .05 I254590 Fishman, 1993

00665 Phosphorus, total mg/L as P .05 I461091 Patton and Truitt, 1992

00666 Phosphorus, dissolved mg/L as P .004 EPA365.1 U.S. Environmental Protection Agency, 1994

00671 Phosphorus, orthophosphate mg/L as P .01 I260190 Fishman, 1993

00915 Calcium, dissolved mg/L as Ca .02 I147287 Fishman and Friedman, 1989

00925 Magnesium, dissolved mg/L as Mg .004 I147287 Fishman, 1993

00930 Sodium, dissolved mg/L as Na .06 I147287 Fishman, 1993

00935 Potassium, dissolved mg/L as K .1 I163085 Fishman and Friedman, 1989

00940 Chloride, dissolved mg/L as Cl .1 I205785 Fishman and Friedman, 1989

00945 Sulfate, dissolved mg/L as SO4 .1 I205785 Fishman and Friedman, 1989

00950 Fluoride, dissolved mg/L as F .1 I232785 Fishman and Friedman, 1989

00955 Silica, dissolved mg/L as SiO2 .1 I270085 Fishman and Friedman, 1989

01046 Iron, dissolved μg/L 6.0 I147287 Fishman, 1993

01056 Manganese, dissolved μg/L .6 I147287 Fishman, 1993

39086 Alkalinity, dissolved, field mg/L as CaCO3 1.0 I203085 Fishman and Friedman, 1989

70300 Dissolved solids, residue at 180oC mg/L 10.0 I175085 Fishman and Friedman, 1989

90095 Specific conductance, laboratory μS/cm 1.0 I278185 Fishman and Friedman, 1989

90410 Acid neutralizing capacity, laboratory mg/L as CaCO3 1.0 I203085 Fishman and Friedman, 1989

Streamflow Characteristics

Three measures of streamflow characteristics were 

computed: low flow, high flow, and flashiness of the 

stream. These measures were the 99-percent exceedance 

flow, the 1-percent exceedance flow, and the standard 

deviation of the data values, respectively (Richter and 

others, 1996). From October 1, 1970 to September 30, 

2003, daily flows were used and statistics computed 

by water year. These flow values were not adjusted 

for drainage area because the temporal analysis only 

compared values for one watershed through time. For 

June 2001 through September 30, 2003, hourly flows 

were used and statistics computed for the entire period. 

Hourly flows were adjusted to normalize for drainage 

area so that flows could be compared across watersheds 

of differing areas. 

Streamflow Characteristics 11
12 Effects of urban land-use change on streamflow and water quality in Oakland County, Michigan, 1970-2003

The continuous streamflow-gaging stations used 

in this analysis are all operated by the U.S. Geological 

Survey. Locations of the streamflow-gaging stations are 

shown in figure 4, and station identification number, the 

name of the stations, and the period of available record 

are listed in table 3. Although daily flows are available 

for most stations throughout 1970–2003, several stations were not operated continuously within the 33-year 

period. Instantaneous data are available for the period 

October 1992 to present (2004). Daily streamflow data 

are not as sensitive to changes in stream response to 

precipitation as instantaneous flows, which are typically 

recorded at 15-minute intervals; however, the record of 

such instantaneous flows only extends back to 1992, 

whereas daily values are available for the entire period of 

record.

Discrete Water-Quality Comparison

During 1966 through 1970, the USGS did some 

limited low-flow water-quality sampling at sites throughout Oakland County. Although this sampling effort was 

limited in both number of samples and chemical scope, 

the results can be used for comparison to data produced 

during 2001 through 2003. Data from each time period 

were grouped by site, and geometric means were calculated for each water-quality characteristic common to 

both sampling efforts. In samples where a specific 

chemical characteristic was not detected at a concentration 

above the MRL listed in table 5, the MRL was used in calculating the geometric mean. These two datasets were then 

compared for increases and decreases in each characteristic 

at each site.

Correlations Among Watersheds Across 

the Urban Gradient

Land-cover and population-density data reflective of the 

year 2000 were collected at the watershed scale and compared to instantaneous streamflow characteristics measured 

between 2000 and 2003, as well as geometric means of 

selected water-quality characteristics at the 14 water- quality sampling locations. This analysis shows most of the 

expected effects of urban land, including effects on streamflow characteristics and water quality.

Changes in Land Cover and Population Density

The results of the land-cover-change analysis on the 

watershed level are presented in table 6. The overall rate 

of land-cover change was most rapid in the least urbanized 

areas, although even the heavily urbanized watersheds of the 

River Rouge watershed continued to develop. The change in 

built landscape is shown at the right side of table 6. 

Table 6. Land-use/land-cover composition of monitored watersheds in Oakland County, Michigan, 1980 to 2000.

[All land-use/land-cover data in percent; refer to figure 4 for station locations]

1 Undifferentiated urban land, not clearly identified as commercial or residential.

Total

Station 

number

Year Built1 Residential Commercial Agricultural Open 

Space

Forest Water Wetland Built Unbuilt

1980 2.4 10.7 1.1 30.5 22.3 13.3 3.5 16.3 14.2 85.8

1985 2.7 10.8 1.1 30.4 22.2 13.2 3.5 16.2 14.5 85.5

04143830 1990 2.6 13.9 1.2 28.4 21.3 13.1 3.5 16.0 17.7 82.3

1995 2.8 15.9 1.2 27.5 20.5 12.6 3.5 16.0 19.9 80.1

2000 3.0 24.8 1.5 17.1 20.3 13.2 3.9 16.2 29.3 70.7

1980 0.7 16.2 1.0 23.9 23.4 17.5 3.5 13.9 17.8 82.2

1985 0.7 17.2 0.9 23.4 23.2 17.3 3.6 13.8 18.8 81.2

04148035 1990 0.6 20.9 1.0 22.3 21.6 17.0 3.6 13.0 22.5 77.5

1995 0.6 24.6 1.2 20.6 19.9 16.1 3.6 13.4 26.5 73.5

2000 1.0 32.8 1.6 12.1 17.8 15.5 3.9 15.4 35.3 64.7

1980 3.6 19.3 2.0 11.5 29.2 7.4 6.0 21.1 24.9 75.1

1985 3.6 20.4 4.5 10.6 26.9 7.3 6.0 20.8 28.5 71.5

04160800 1990 4.1 24.1 5.7 9.1 23.6 6.9 6.0 20.5 33.9 66.1

1995 4.9 27.5 5.6 6.9 22.0 6.6 6.0 20.5 38.0 62.0

2000 6.8 36.1 7.3 3.9 15.7 5.1 6.0 19.1 50.2 49.8
Table 6. Land-use/land-cover composition of monitored watersheds in Oakland County, Michigan, 1980 to 2000.

[All land-use/land-cover data in percent; refer to figure 4 for station locations]

1 Undifferentiated urban land, not clearly identified as commercial or residential.

Total

Station 

number

Year Built1 Residential Commercial Agricultural Open 

Space

Forest Water Wetland Built Unbuilt

1980 4.6 27.8 4.8 9.0 22.7 10.2 8.5 12.5 37.2 62.8

1985 4.7 28.6 5.6 8.6 21.7 10.0 8.6 12.3 38.9 61.1

04160900 1990 4.8 32.3 6.2 7.7 18.8 9.5 8.6 12.2 43.2 56.8

1995 5.0 36.1 6.4 6.6 16.6 8.6 8.6 12.1 47.5 52.5

2000 6.1 43.8 7.9 3.6 11.8 6.6 8.6 11.6 57.7 42.3

1980 5.3 32.0 9.6 6.3 18.1 7.6 10.2 11.0 46.8 53.2

1985 5.5 32.6 9.9 6.0 17.4 7.5 10.2 10.9 48.0 52.0

04161000 1990 5.2 35.3 10.9 5.4 15.1 7.2 10.2 10.7 51.4 48.6

1995 5.6 38.4 11.0 4.6 13.3 6.4 10.2 10.5 55.0 45.0

2000 6.4 44.3 12.5 2.5 9.3 4.8 10.3 10.0 63.1 36.9

1980 4.6 19.8 1.9 23.8 25.8 9.0 3.4 11.7 26.3 73.7

1985 4.5 23.1 2.0 22.7 23.8 8.8 3.5 11.5 29.6 70.4

04161540 1990 4.9 27.3 2.3 20.7 21.1 8.5 3.7 11.4 34.6 65.4

1995 4.9 33.7 2.4 17.4 18.0 8.2 3.9 11.4 41.1 58.9

2000 5.4 43.9 3.1 9.2 15.3 7.1 4.0 11.9 52.4 47.6

1980 1.2 10.3 0.3 24.4 30.5 15.1 3.8 14.6 11.7 88.3

1985 1.1 13.1 0.2 23.3 29.2 14.9 3.7 14.5 14.4 85.6

04161580 1990 1.2 19.0 0.4 20.8 26.6 14.1 3.7 14.3 20.5 79.5

1995 1.2 19.6 0.4 21.1 25.5 14.1 3.7 14.4 21.1 78.9

2000 3.0 24.7 2.4 13.2 22.1 16.0 4.3 14.2 30.2 69.8

1980 5.9 23.5 5.6 14.5 23.8 9.5 6.0 11.1 35.1 64.9

1985 6.1 25.2 6.0 14.0 22.4 9.2 6.1 11.0 37.3 62.7

04161810 1990 6.1 28.6 7.0 12.6 19.9 8.8 6.2 10.8 41.7 58.3

1995 6.3 31.9 7.2 11.2 18.0 8.3 6.3 10.7 45.4 54.6

2000 7.1 38.4 8.9 7.3 13.9 7.6 6.4 10.6 54.3 45.7

1980 4.7 57.7 7.3 3.3 12.0 5.3 4.6 5.0 69.8 30.2

1985 4.9 58.4 7.6 3.0 11.8 4.9 4.6 4.9 70.9 29.1

04166000 1990 5.5 64.1 9.2 1.4 6.7 3.8 4.6 4.7 78.8 21.2

1995 5.5 68.2 9.8 0.4 4.8 2.7 4.8 3.8 83.5 16.5

2000 5.7 70.0 10.7 0.4 2.8 2.3 4.8 3.4 86.3 13.7

1980 6.0 61.2 7.8 2.3 10.6 5.8 2.8 3.6 75.0 25.0

1985 6.1 61.6 8.0 2.1 10.4 5.5 2.8 3.6 75.6 24.4

04166100 1990 6.6 65.8 9.7 0.8 6.3 4.5 2.8 3.4 82.1 17.9

1995 6.5 68.2 10.2 0.4 5.0 3.8 2.9 3.1 84.9 15.1

2000 6.7 70.6 11.0 0.2 2.7 3.2 2.9 2.8 88.3 11.7

1980 6.5 64.1 18.4 0.0 7.2 3.0 0.0 0.7 89.0 11.0

1985 6.3 64.2 18.7 0.0 7.1 3.0 0.0 0.7 89.2 10.8

04166200 1990 7.6 65.0 19.8 0.0 3.9 3.0 0.0 0.7 92.4 7.6

1995 7.8 65.4 20.4 0.0 2.7 3.0 0.0 0.7 93.6 6.4

2000 8.1 65.7 21.0 0.0 1.8 2.8 0.0 0.7 94.8 5.2

Changes in Land Cover and Population Density 13
14 Effects of urban land-use change on streamflow and water quality in Oakland County, Michigan, 1970-2003

Table 6. Land-use/land-cover composition of monitored watersheds in Oakland County, Michigan, 1980 to 2000.

[All land-use/land-cover data in percent; refer to figure 4 for station locations]

1 Undifferentiated urban land, not clearly identified as commercial or residential.

Total

Station 

number

Year Built1 Residential Commercial Agricultural Open 

Space

Forest Water Wetland Built Unbuilt

1980 5.3 35.5 7.5 6.7 25.9 11.2 0.4 7.5 48.2 51.8

1985 5.0 37.5 7.8 6.5 25.0 10.4 0.5 7.3 50.3 49.7

04166315 1990 4.2 50.5 9.9 4.6 15.9 8.2 0.5 6.3 64.5 35.5

1995 4.0 53.2 11.5 3.0 14.0 7.7 0.5 6.2 68.7 31.3

2000 5.4 61.2 13.2 0.8 7.5 5.2 0.5 6.2 79.8 20.2

1980 4.2 20.6 3.1 12.7 24.3 12.0 7.8 15.4 27.8 72.2

1985 4.4 21.2 3.1 12.3 24.0 11.9 7.8 15.2 28.7 71.3

04170000 1990 4.3 26.6 3.7 10.8 20.4 11.0 8.0 15.0 34.7 65.3

1995 4.7 29.4 4.2 9.5 18.8 10.4 8.1 14.9 38.3 61.7

2000 6.3 36.2 6.3 5.9 13.4 9.4 8.2 14.3 48.9 51.1

1980 4.8 19.6 2.8 12.7 24.9 12.8 8.1 14.2 27.3 72.7

1985 5.0 20.2 2.9 12.4 24.6 12.7 8.3 14.0 28.1 71.9

04170500 1990 4.9 25.6 3.5 10.9 21.0 11.7 8.6 13.8 34.0 66.0

1995 5.3 28.5 3.9 9.6 19.3 11.1 8.6 13.7 37.7 62.3

2000 6.9 35.1 6.0 6.2 14.0 9.9 8.7 13.2 48.1 51.9

Population-density estimates by watershed are 

given in table 7. Three important results can be 

observed in these estimates. First, overall population 

density is increasing in every watershed, the simple 

result of more people living in the fixed watershed area. 

Second, the population density in residential land is 

decreasing in several watersheds. In other words, the 

amount of land classified as residential is expanding 

more rapidly than the population is expanding. Third, 

the increase in population and increase in residential 

land by decade can be used to generate an estimate of 

the average density of residential space constructed during the period. These densities are also decreasing in 

most watersheds. The key assumption of this estimate 

is that the population density of the existing residential 

space is unchanged. These results indicate a consistent 

trend towards lower-density development, particularly 

in watersheds where development has been recent.

Table 7. Estimated population densities in gaged watersheds in Oakland County, Michigan, 1980 to 2000.

[Refer to figure 4 for station locations]

Station number Year Residential acres Residential 

(square miles)

Estimated population Residential population density 

(persons per acre)

1980 2,061 3.22 11,200 5.44 

04143830 1990 2,815 4.40 12,500 4.44 

2000 4,920 7.69 16,000 3.25 

1980 1,749 2.73 7,280 4.16 

04148035 1990 2,310 3.61 8,570 3.71 

2000 3,606 5.63 10,800 2.99 
Table 7. Estimated population densities in gaged watersheds in Oakland County, Michigan, 1980 to 2000.

[Refer to figure 4 for station locations]

Station number Year Residential acres Residential

 (square miles)

Estimated population Residential population density 

(persons per acre)

1980 1,488 2.33 11,000 7.39 

04160800 1990 1,856 2.90 12,200 6.57 

2000 2,746 4.29 18,033 6.57 

1980 7,906 12.35 67,900 8.59 

04160900 1990 9,187 14.35 72,200 7.86 

2000 12,444 19.44 89,400 7.18 

1980 14,264 22.29 174,000 12.20 

04161000 1990 15,737 24.59 177,000 11.25 

2000 19,736 30.84 195,000 9.88 

1980 5,119 8.00 38,900 7.60 

04161540 1990 7,141 11.16 49,200 6.89 

2000 11,369 17.76 66,500 5.85 

1980 955 1.49 3,720 3.89 

04161580 1990 1,727 2.70 3,940 2.28 

2000 2,179 3.40 4,980 2.29 

1980 25,708 40.17 274,000 10.66 

04161810 1990 31,452 49.14 297,000 9.44 

2000 41,966 65.57 347,000 8.27 

1980 7,689 12.01 71,500 9.30 

04166000 1990 8,505 13.29 76,600 9.01 

2000 9,266 14.48 82,400 8.89 

1980 19,232 30.05 175,000 9.10 

04166100 1990 20,637 32.24 188,000 9.11 

2000 22,138 34.59 202,000 9.12 

1980 2,369 3.70 31,500 13.30 

04166200 1990 2,397 3.74 31,500 13.14 

2000 2,420 3.78 32,400 13.39 

1980 2,078 3.25 20,100 9.67 

04166300 1990 3,145 4.91 32,200 10.24 

2000 3,876 6.06 41,600 10.73 

1980 2,587 4.04 27,500 10.63 

04166315 1990 3,667 5.73 39,500 10.77 

2000 4,438 6.93 49,800 11.22 

1980 10,425 16.29 76,143 7.30 

04170000 1990 13,527 21.14 83,900 6.20 

2000 18,255 28.52 103,000 5.64 

1980 11,122 17.38 78,440 7.05 

04170500 1990 14,537 22.71 86,600 5.96 

2000 19,786 30.92 107,000 5.41 

Changes in Land Cover and Population Density 15
16 Effects of urban land-use change on streamflow and water quality in Oakland County, Michigan, 1970-2003

The majority (55 percent) of land converted to urban 

uses was classified as open space in 1980, with forested 

and agricultural land accounting for an additional 42 

percent (14 percent and 28 percent, respectively). Forest 

lands were nearly evenly divided between hardwoods 

and conifers (49 and 51 percent, respectively). The 

dominant agricultural use was cropland (85 percent of 

agricultural land), with small fractions classified as permanent pasture (8 percent) or livestock (4 percent).

Correlation Between Land Cover and 

Streamflow Across the Urban Gradient

In table 8, the Spearman rank-order correlation (p) 

coefficient and significance (p-value) for each correlation are listed. Residential and commercial land covers, as well as total percent built, watershed population 

density, and residential density all show significant 

positive correlations with high flows and flashiness but 

no significant correlations with low flows. Agricultural, 

open space, water, and wetland land covers show significant negative correlations with peak flows and flashiness but again, are not significantly correlated with low 

flows. The correlations with high flows and flashiness 

are well described in the literature; however, the absence 

of significant correlations with low flows may indicate 

that other factors, such as ground water pumping in rural 

areas or transfers of water into urban watersheds, are 

offsetting the anticipated decline in low flows as a result 

of decreased infiltration in urban areas.

Table 8. Spearman Rho values correlating specific characteristics 

of daily streamflow to land-cover and urbanization metrics across 

an urban gradient.

[Bold type indicates correlation significant at the 0.99 confidence level; --, 

correlation not significant at the 0.95 confidence level]

1 Flashiness calculated as standard deviation of daily flow

Exceedance flow

Land use/land cover 1% 99% Flashiness1

Residential 0.77 -- 0.78

Commercial 0.66 -- 0.69

Agricultural -0.60 -- -0.63

Open Space -0.73 -- -0.74

Forest -0.56 -- -0.64

Water -- -- -0.54

Wetland -0.84 -- -0.79

Built percentage 0.74 -- 0.76

Overall population density 0.77 -- 0.81

Residential population density 0.68 -- 0.74

Correlation Between Land Cover and Water 

Quality Across the Urban Gradient

Eight land-cover types and three measures of residential land-use intensity were compared to geometric 

means of water-quality characteristics measured at the 14 

sample sites. Spearman rank-order correlation coefficients with p-values higher than 0.95 are listed in table 9. 

In general, the strongest correlations were with salt 

ions (sodium, potassium, and chloride). These ions were 

all strongly positively correlated with residential and 

commercial land covers, as well as overall percentage of 

the watershed built, population density, and residential 

population density. These ions were negatively correlated with agriculture, open space, forest, and wetland 

land covers. These correlations are also observed in 

the overall specific conductance, an indicator of salts in 

solution. Noncarbonate hardness, the combined concentration of calcium and magnesium ions that are in excess 

of the carbonate and bicarbonate ion concentration, also 

showed strong positive correlations with the development metrics and negative correlations with agriculture, 

open space, forest, and wetland land covers. These 

correlations may suggest some base leaching from soils 

in urbanized areas, resulting from exposure of carbonate 

soils to elevated concentrations of sodium and potassium.

Few significant correlations were observed between 

any of the land-use classes and nutrients. A weak correlation was observed between total organic nitrogen 

plus total ammonia and both commercial land use (Rho 

= 0.61, p < 0.05) and residential population density (Rho 

= 0.61, p < 0.05). Dissolved ammonia did correlate 

strongly with residential population density (Rho = 0.72, 

p < 0.01). 

E. coli concentrations were strongly correlated with 

all of the development metrics and strongly negatively 

correlated with agriculture, open space, forest, and wetlands. 

Changes in Streamflow and Water 

Quality Through Time

Annual high flows, low flows, and variability in 

stream flow were analyzed for 14 watersheds with 

headwaters in Oakland County. Five watersheds showed 

significant trends in low flows, one watershed showed 

a significant trend in peak flows, and none showed a 

significant trend in variability over the 33-year period. 

Relatively little change was observed in water chemistry, 

although phosphorus and sulfate concentrations were 

generally lower and chloride concentrations were generally higher in the 2001–2003 sampling compared to the 

1966–1970 sampling.
Table 9. Spearman rank-order correlations between water-quality parameters and land-use/land-cover metrics.

[All Rho values shown are significant at the 0.95 confidence level. Rho values in bold type are significant at the 0.99 confidence level; --, Rho value not 

significant at the 0.95 confidence level; oC, degrees Celsius]

Land-cover type Urbanization

Water-quality characteristics

Built

Residential

CommercialAgriculturalOpen Space

Forest

Open Water

Wetland

Percent built

Population density

Residential population density

Specific conductance -- 0.89 0.89 -0.84 -0.93 -0.74 -- -0.89 0.92 0.93 0.91

Dissolved oxygen -- -- -- -- -- -- -- -- -- -- --

pH -- -- -- -- -- -- -- -- -- -- --

Alkalinity -- -- -- -- -- -- -0.75 -- -- -- --

Dissolved solids, residue on evaporation at 180oC -- 0.85 0.84 -0.78 -0.88 -0.68 -- -0.85 0.86 0.89 0.86

Dissolved ammonia -- 0.58 0.71 -0.65 -0.69 -0.64 -- -- 0.67 0.64 0.72

Dissolved nitrite -- -- -- -- -- -- -- -- -- -- --

Dissolved organic nitrogen and ammonia -- -- -- -- -- -- -- -- -- -- --

Total organic nitrogen and ammonia -- -- 0.61 -- -- -- -- -- -- -- 0.61

Dissolved nitrite and nitrate -- -- -- -- -- -- -- -- -- -- --

Total phosphorus -- -- -- -- -- -- -- -- -- -- --

Dissolved phosphorus -- -- -- -- -- -- -- -- -- -- --

Dissolved orthophosphate 0.78 -- 0.70 -0.58 -0.69 -- -- -0.67 0.60 0.58 0.64

Hardness -- 0.60 -- -- -- -- -0.70 -0.62 0.61 0.61 --

Non-carbonate hardness -- 0.81 0.77 -0.6967 -0.75 -0.72 -- -0.77 0.81 0.86 0.81

Dissolved calcium -- 0.67 -- -0.60 -0.58 -0.61 -0.69 -0.56 0.68 0.69 0.60

Dissolved magnesium -0.63 -- -- -- -- -- -0.58 -- -- -- --

Dissolved sodium 0.57 0.89 0.93 -0.90 -0.96 -0.78 -- -0.90 0.92 0.92 0.91

Dissolved potassium -- 0.80 0.84 -0.75 -0.87 -0.67 -- -0.83 0.79 0.85 0.85

Dissolved chloride -- 0.89 0.95 -0.90 -0.96 -0.79 -- -0.90 0.93 0.94 0.94

Dissolved sulfate -- 0.85 0.79 -0.77 -0.82 -0.75 -- -0.82 0.82 0.83 0.78

Dissolved fluoride -- -- 0.69 -- -0.65 -0.37 -- -0.66 0.55 0.61 0.67

Dissolved silica -0.73 -- -- -- -- -- -0.79 -- -- -- --

Dissolved iron -- -0.54 -- -- 0.64 -- -- 0.73 -0.58 -0.56 --

Dissolved manganese -- -- -- -- -- -0.56 -0.63 -- -- -- --

E.coli count (colonies/100 mL) -- 0.81 0.84 -0.78 -0.78 -0.73 -- -0.83 0.87 0.85 0.84

Changes in Streamflow Characteristics

Comparable land-cover data are not available prior 

to 1980. Given the rates of land-cover change documented in table 6 between 1980 and 2000 and previous 

studies of the effect of land-cover change on streamflow, 

an increase in peak flows and flashiness, accompanied 

by a decrease in low flows, would be expected. However, relatively little change was observed. The Spearman Rho values for each flow statistic at each streamflow-gaging station are shown in table 10.

Four watersheds showed statistically significant ( p 

< 0.10) negative trends in the daily 99-percent-exceedance flow. These four watersheds are all moderately 

developed, and substantial portions of each meet their 

water needs from ground water. Observation wells at the 

Changes in Streamflow Characteristics 17
18 Effects of urban land-use change on streamflow and water quality in Oakland County, Michigan, 1970-2003

Table 10. Spearman rank-order correlations between year and 

selected annual daily-streamflow statistics for water years 1970 

through 2003. 

1 Annual peak flows were used during the period this site was operated as a crest-stage gage

99-percent 

exceedance

Standard 

deviation

1-percent 

exceedance

Station 

number

Rho p-value Rho p-value Rho p-value

04143900 0.16 0.41 0.00 0.99 -0.14 0.47

04148140 -0.33 0.06 -0.13 0.47 -0.15 0.39

04160800 -0.15 0.38 -0.13 0.46 -0.10 0.58

04160900 -0.55 0.02 0.09 0.60 0.06 0.74

041610001 -- -- -- -- 0.49 0.02

04161540 -0.24 0.17 -0.06 0.72 0.01 0.97

04161580 -0.21 0.24 -0.26 0.14 -0.29 0.09

04164000 -0.60 0.01 -0.11 0.54 0.15 0.40

04166000 0.50 0.00 0.29 0.10 0.31 0.08

04166100 0.56 0.00 0.11 0.55 0.11 0.53

04166200 0.18 0.30 0.14 0.39 0.10 0.57

04166300 -0.55 0.00 0.09 0.60 0.06 0.74

04170000 0.05 0.77 0.00 0.99 0.08 0.64

Commerce, White Lake, and Groveland Townships have 

all reported record depths to ground water during fall 

2003 (Blumer and others, 2003). The absence of a corresponding increase in peak flows or variability suggests 

that this decrease in low flows is the result of increased 

ground-water demand rather than a shift from infiltration to runoff. Two watersheds in southern Oakland 

County showed statistically significant positive trends in 

this low-flow metric. These upward trends are likely the 

result of (1) municipalities shifting from ground water to 

surface water as a source of water supply, and (2) leakage from the delivery mains. These two watersheds are 

heavily developed and are supplied with approximately 

125 Mgal/d of surface water from Lake Huron by the 

Detroit Water and Sewer Department (C.L. Luukkonen, 

U.S. Geological Survey written commun., 2004). Twenter and Knutilla (1972) documented more than 100 feet 

of drawdown in the Pontiac area during the 1960s as a 

result of pumping for public water supply. Although 

no current observation data are available for monitoring wells in the Pontiac area, an observation well at the 

Cranbrook Academy has recovered and actually began 

flowing at land surface since it was last monitored in the 

1970s.

No watershed showed a statistically significant 

trend in the variability of daily flows. One watershed, 

the Clinton River at Auburn Hills (04161000), showed 

a significant positive trend in the 1-percent exceedanceflow, a measure of high flow. Although a clear trend 

exists in the 1-percent-exceedance flow data up to 1982, 

no significant difference exists between the 1-percentexceedance flows for 2001–2003 and the data collected 

prior to 1982.

Changes in Stream-Water Quality Through Time

Comparisons between the 1960s low-flow samples 

and low-flow samples collected in 2002 and 2003 are 

shown in figures 5a-f. The values for each time period 

were computed by calculating the geometric mean of the 

low-flow samples in each time period. For purposes of 

describing an increase or decrease, a difference of more 

than 10 percent of the concentration from the 1966–1970 

mean is considered a change; a difference of less than 10 

percent is considered no change. Specific conductance 

increased at 12 of 13 sites for which prior data are available, but it remained unchanged at one site (04143830). 

Concentrations of chloride increased at every site, and 

more than doubled at 11 of the 13 sites for which prior 

data are available.

Concentrations of phosphorus decreased at seven 

of the nine sites for which prior data were available but 

increased at the two remaining sites (04148035 and 

04166315). Concentrations of sulfate decreased at 8 

of 13 sites for which prior data were available while 

remaining unchanged at 5. Data from the National 

Atmospheric Deposition Network (2004) indicate that 

atmospheric deposition of sulfur has decreased dramatically over the past 20 years in the Upper Midwest. The 

two sites where sulfate concentrations are unchanged are 

on the Huron River downstream from the City of Milford Wastewater Treatment Plant. Nitrate concentrations 

increased at 5 of 12 sites for which data are available 

while decreasing at 5 and remaining unchanged at 2. 

Nitrate concentrations more than doubled at three sites, 

04161000, 04161810, and 04166315. Noncarbonate 

hardness increased at 9 of 14 sites, was unchanged at 3 

sites, and decreased at 2 sites.

Increased chloride concentrations, and accompanying increases in specific conductance, are an anticipated 

effect of urbanization. The decreases in nutrients and 

sulfate concentrations, combined with little change in 

the streamflow characteristics, contradict the expected 

effects of urbanization based on the literature.
Figure 5. Comparison of geometric mean (a.) specific conductance, (b.) chloride concentration, (c.)phosphorous concentration, (d.) 

sulfate concentration, (e.) nitrate concentration, and (f.) noncarbonated hardness of discrete water-quality samples collected during 

low flow conditions, 1966-1970 and 2001-2003.

Changes in Stream-Water Quality Through Time 19

0

200

400

600

800

1,000

1,200

1,400

1,600

0414383004148035

04160800

04160900

0416100004161540

04161580041618100416600004166100

04166315

0417000004170500

50

100

150

200

250

300

04143830041480350416080004160900041610000416154004161580041618100416600004166100

0416631504170000

04170500

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

041438300414803504161000041615400416181004166000041663150417000004170500

1966-1970

2001-2003

STATION NUMBER

DISSOLVED PH

OSPH

O

R

O

US CO

NCENTR

ATIO

N,

IN

MILIG

R

A

MS PER LITER

AS PH

OSPH

O

R

O

US

0

20

40

60

80

100

120

140

041438300414803504161000041615400416181004166000041663150417000004170500

STATION NUMBER

04166100041615800416090004160800

SULFATE CO

NCENTR

ATIO

N, IN

MILLIG

R

A

MS PER LIETER

0

20

40

60

80

100

120

140

160

180

04143830041480350416080004160900041610000416154004161580

04161810

041660000416610004166200041663150417000004170500

STATION NUMBER

N

O

NCA

RBO

N

ATE H

A

R

D

NESS, IN

MILLIG

R

A

MS PER LITER

AS CACO3

0

1

2

3

4

5

6

7

8

0414383004148035

04160800

0416100004161540

041615800416181004166000

04166100

04166315

04170000

04170500

STATION NUMBER

NITR

ATE CO

NCENTR

ATIO

N, IN

MILLIG

R

A

MS

PER LITER

AS NITR

O

GEN

STATION NUMBER

SPECIFIC CO

N

D

UCTA

NCE, IN

MICR

OSIEMENS

PER CENTIMETER

AT 25 DEG

REES CELSIUS

STATION NUMBER

CHLO

RIDE CO

NCENTR

ATIO

N, IN

MILLIG

R

A

MS PER LITER

1966-1970

2001-2003

1966-1970

2001-2003

1966-1970

2001-2003

1966-1970

2001-2003

1966-1970

2001-2003

(a.) (b.)

(c.) (d.)

(e.) (f.)
20 Effects of urban land-use change on streamflow and water quality in Oakland County, Michigan, 1970-2003

Summary and Conclusions

The U.S. Geological Survey, in cooperation with 

Oakland County, Mich., evaluated streamflow and waterquality records for 14 watersheds in Oakland County, 

Michigan, using temporal and urban gradient analysis methods to identify effects of land-use change on 

streamflow and water-quality characteristics from 1970 

to 2003. Each type of analysis method uses a different 

type of data and is sensitive to different effects. Available land-cover and population data were standardized to 

be consistent through time, and dasymetric mapping was 

used to distribute population data (collected at the minor 

civil division level) to watershed boundaries. These 

standardized land cover and population datasets were 

used to quantify change in land-cover and population 

across the 14 watersheds studied. The effects of landcover change on hydrologic systems, particularly the 

effects on streamflow characteristics and water quality, is 

a concern in Michigan and across the United States.

Clear, monotonic, and substantial increases in urban 

land covers have taken place in every one of the 14 study 

watersheds. In some cases, these increases represent 

conversion of more than 25 percent of the total watershed area to urban use from1980–2000. Analysis of 

these urbanization patterns indicates that although population density and urban land covers in already urbanized 

areas are increasing, urban land covers are increasing 

more rapidly in less-developed areas. In these lessdeveloped areas, the trend is also consistently towards 

lower-density residential space.

On the basis of previous studies, several changes 

in streamflow and water quality would be expected. 

First, an increase in high flows would be expected. This 

change was observed in only one of the 14 watersheds, 

but the flow-measurement site was inactive for a substantial period, which may affect the result. The daily 

streamflow data used in the time-series analysis are less 

sensitive to changes in high flows than instantaneous 

streamflow data are, and this may account for the lack of 

an observable trend; however, changes in development 

practices during the period from 1970-2003, particularly 

the trend to larger residential lots and requirements for 

storm-water management, may have damped the effects 

of urbanization on high flows.

Second, a decrease in low flows would be expected. 

This change was observed in 3 of the 14 watersheds 

(at streamflow-gaging stations 04148140, 04160900, 

04166300). Ground-water levels were measured in two 

of these watersheds, and new period-of-record lows were 

recorded during summer 2003. In 2 of the 14 watersheds 

(at stations 04166000 and 04166100) significant positive 

trends in low flows were found. These two watersheds 

are heavily developed and supplied with surface water 

from the Detroit Water and Sewerage Department

(DWSD) at a rate of approximately 125 Mgal/d. Leakage from both the supply and removal systems likely 

contributes water to streamflow. Ground water has 

ceased to be a significant source of water supply in 

these watersheds. The combination of leakage from 

the DWSD system and the gradual recovery of the local 

ground-water system following the cessation of pumping in the 1960s likely explains the positive trend in low 

flows in these two watersheds.

Third, increased urbanization, including increased 

amount of impervious surface, would be expected to 

result in increased variability in streamflows, commonly 

referred to as flashiness. None of the 14 watersheds 

studied showed any significant trend in daily streamflow 

variability over the 33-year period studied. As discussed 

previously, daily data are not as sensitive to change as 

instantaneous data. Changes in development practices 

from 1970–2003, particularly the trend to larger residential lots and requirements for stormwater management, 

may have damped the effects of urbanization on variability.

Anticipated changes in water quality included 

increases in most measured characteristics, including 

dissolved solids, specific conductance, chloride, nitrogen, phosphorus, and sulfate. Only total dissolved solids 

and chloride concentrations increased at all sites. Sulfate 

and phosphorus concentrations decreased at most sites, 

although the decreased concentrations of sulfate are most 

likely due to decreased atmospheric deposition. Nitrate 

concentrations were largely unchanged.

The gradient analysis showed similar results with 

regard to water-quality characteristics. Although relatively few correlations existed between any land cover 

and nutrients, urban land covers and measures of urbanization were all highly positively correlated with specific 

conductance, dissolved solids, noncarbonate hardness, 

sodium, potassium, chloride, sulfate, and number of E. 

coli bacteria. With regard to streamflow characteristics, 

the gradient analysis showed highly significant positive 

correlations between the urban land covers and high 

flows as well as variability, but no significant correlation 

with low flows.

Although the effects of cumulative urbanization 

can be clearly correlated with increased peak flows 

and variability as well as increases in concentrations of 

salts, sulfate, phosphorus, nitrogen, and E. coli, within 

the framework of an urban gradient, the data collected 

in the last several decades show relatively few of these 

changes, with the exception of the increase in salts. 

The data sets available for both temporal and gradient analyses are limited in several ways. Whereas temporal methods enable one to evaluate incremental effects 

through time but are limited by the relatively lower 

quality hydrologic data available, gradient analysis only 

measures the cumulative effect of changes throughout 
Summary and Conclusions 21

time including potentially different regulatory and design 

environments. Given the data available, relatively few of 

the anticipated changes in streamflow and water quality 

associated with land-cover change are observable from 

1970–2003. Although the absence of these changes 

may be the result of increased stormwater management 

requirements and changes in development patterns, it is 

also possible that the changes are not detectable with the 

data available. The gradient analysis identifies most of 

the anticipated effects of land-cover change but does not 

differentiate when the change occurred. 

In next decade, sufficient periods of record for 

land-cover, streamflow, and water-quality data should be 

available to allow more comprehensive temporal analysis 

of the higher-quality hydrologic data over an incremental 

timeframe.

Acknowledgments

The author thanks Ann Burns and Lisa Mayoras, 

both with the Southeast Michigan Council of Governments, for making available population and land-cover 

data, respectively. The author also appreciates the assistance, comments, and recommendations of Dr. Thomas 

J. Gordon and the staff of the Oakland County Department of Human Services.

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Stephen S. Aichele -- Effects of Urban Land-Use Change on Streamflow and Water Quality in Oakland County, Michigan, 1970-2003 -- Scientific Investigations Report 2005-5016

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