Integrating water quality management & landuse planning in a watershed context

Journal of Environmental Management (2001) 61, 25–36
doi:10.1006/jema.2000.0395, available online at http://www.idealibrary.com on
Integrating water-quality management
and land-use planning in a watershed
context
X. Wang
The spatial relationships between land uses and river-water quality measured with biological, water chemistry,
and habitat indicators were analyzed in the Little Miami River watershed, OH, USA. Data obtained from
various federal and state agencies were integrated with Geographic Information System spatial analysis
functions. After statistically analyzing the spatial patterns of the water quality in receiving rivers and land
uses and other point pollution sources in the watershed, the results showed that the water biotic quality did
not degrade significantly below wastewater treatment plants. However, significantly lower water quality was
found in areas downstream from high human impact areas where urban land was dominated or near point
pollution sources. The study exhibits the importance of integrating water-quality management and land-use
planning. Planners and policy-makers at different levels should bring stakeholders together, based on the
understanding of land–water relationship in a watershed, to prevent pollution from happening and to plan
for a sustainable future.
© 2001 Academic Press
Keywords: water quality, land-use planning, watershed management, Geographic Information
Systems, Index of Biotic Integrity, Invertebrate Community Index.
Introduction
Industrialization and urbanization have
brought prosperity, and at the same time,
also have resulted in many environment
problems. It has been recognized that the
quality of receiving waters is affected by
human activities in a watershed via point
sources, such as wastewater treatment
facilities, and non-point sources, such as
runoff from urban area and farm land.
Although researchers have paid particular
attention to the effect of land use on water
quality (Lenat and Crawford, 1994; Hall
et al., 1994), a water-quality component often
is missing in land-use plans and land-use
planning is rarely used in water-quality
management. This could be due to the fact
that water-quality management and landuse
planning often are administrated by
different agencies that do not coordinate
constantly. Most planning agencies and local
Email of author: xinhao.wang@uc.edu
authorities do not have resources to collect
extensive land use and water-quality data in
developing plans (Wang and Yin, 1997) and
water-quality management agencies traditionally
address existing water-quality problems
rather than preventing them.
Water quality refers to the physical, biological
and chemical status of the water
body. Streams and rivers are typically diverse
and biologically productive environments in
their natural form. The presence, abundance,
diversity and distribution of aquatic species
in surface waters are dependent upon a myriad
of physical and chemical factors, such
as temperature, suspended solids, pH, nutrients,
chemicals, and in-stream and riparian
habitats. Until recently, the dominant methods
of evaluating water quality are based on
water chemical and, to some extent, physical
properties. Studies have found that biological
impacts from non-point sources and habitat
degradation may not be fully represented by
the periodical measurements of the physical–chemical
characteristics of water bodies.
School of Planning,
University of Cincinnati,
Cincinnati, OH
45221-0016, USA
Received 19 April 2000;
accepted 5 October 2000
0301–4797/00/010025C12 $35.00/0
© 2001 Academic Press

26 X. Wang
For example, the Ohio Environmental Protection
Agency (OEPA) used both water chemistry
and biological indicators to evaluate
water quality and discovered that the amount
of impaired waters was twice the amount if
chemical indicators were used alone (OEPA,
1988). To detect the effects of human activities
which were missed or underestimated
by the conventional physical-chemical indicators,
methods of biological assessment were
developed in the 1970s and early 1980s
(Norris and Norris, 1995; OEPA, 1987, 1989).
Biological assessment of water quality is
based on the assumption that a water body
with biological integrity should have the
ability to ‘support and maintain a balanced,
integrated, adaptive community of organisms
having a species composition, diversity,
and functional organization comparable
to that of the natural habitats within
a region’ (Karr and Dudley, 1981). Therefore,
those water bodies that have been
impacted by human activities to various
degrees should demonstrate changes in biological
integrity. Since the US Environmental
Protection Agency (USEPA) issued guidelines
for state environmental protection agencies
to develop and implement biological
assessment of surface-water quality (USEPA,
1990), biological assessment has been used
in various aquatic environments, such as
streams, lakes and estuaries. Various organisms
including fish, insects, macroinvertebrates,
and algae (especially diatoms) have
been used in these studies, using population
size, species composition or community
structure, and various activities as indicators
of water quality (Angermier and Karr, 1986;
Elnaggar et al., 1997). Biological assessment
of water quality has proven to be a useful complementary
tool to the conventional physicalchemical
assessment for a wide variety of
human impacts, including urban development
(Khan, 1991; Norris and Norris, 1995).
OEPA has been a leader in creating biological
criteria as the operative standards for
evaluating water-quality status by developing
Index of Biotic Integrity (IBI) for fish
communities and Invertebrate Community
Index (ICI) for invertebrates.
Although the impacts of human activities
on environment have been discussed
and debated extensively within conceptual
and moral contexts, there is much need for
more empirical analyses. This paper presents
a study exploring the spatial dependence
of water quality measured with water
chemistry, biological and habitat indicators
and land uses, using spatial statistical analyses
based on Geographic Information Systems
(GIS), in the Little Miami River (LMR)
watershed, OH. After examining the complexity
of water-quality indicators and the
relationship between the quality of receiving
rivers and land uses in the watershed, the significance
of integrating water-quality management
and land-use planning is discussed.
Although the data availability limits the size
of data set used in the study, the results
reveal some patterns that are too significant
to be ignored in watershed management.
Study-area
The Little Miami River watershed is located
in southwest Ohio, adjacent to the greater
metropolitan Cincinnati area (Figure 1). The
LMR drains an area of 4550 square kilometers
and has a main stem length of
170 km. The northern half of the watershed
is located in the Eastern Corn Belt Plains
ecoregion (Omernik, 1988), which is characterized
by level to gently sloping land. Coarse
glacial deposits (e.g. gravel, cobble, and boulders)
dominate substrates in this region. The
southern half of the watershed is located in
the Interior Plateau ecoregion and is characterized
by higher gradient streams with
bedrock (limestone and shale) substrates.
According to the land-use data compiled by
the Ohio Department of Natural Resources
(ODNR), the LMR watershed is primarily
dominated by cropland and pasture (71Ð0%).
The second largest land use is wooded area
(22Ð8%) with the urban land as the third
(4Ð2%). The largest urban areas in the watershed
are on the western side, which forms the
eastern boundary of the growing metropolitan
areas from Dayton to Cincinnati, OH.
The LMR watershed contains a major recreational
area and the most rapidly growing
part of the state of Ohio. During the period
from 1990–1997, population in four of the
five counties which make up the majority
of the LMR watershed increased by a range
of 15–25%, compared to state wide increase
of only 3Ð1%. Projected population growth in
this area will take the Cincinnati Standard
Metropolitan Area (SMA) to over 2 000 000

Water-quality and land-use planning 27
N
Springfield
CLARK
MADISON
BUTLER
HAMILTON
MONTGOMERY
Dayton
TURTLE CR
WARREN
LITTLE MIAMI R
GREENE
CAESAR CR
STONELICK CR
LITTLE MIAMI R
CLINTON
LITTLE MIAMI R TODD FK
LITTLE MIAMI R EFK
FAYETTE
HIGHLAND
Cincinnati
CLERMONT
BROWN
50 0 50
kilometers
Figure 1. Study area: Little Miami River Watershed, OH, USA. Little Miami River (...); Little Miami River
Barin (—); urban area, .
by the year 2000. As a result, development
pressure in the basin is extreme.
The LMR is a designated National and
State Scenic River as well as an Exceptional
Warmwater Habitat. The river is biologically
diverse in fish, mussels, macroinvertebrates,
and algae. An OEPA study indicated that
although total annual loading from point
sources has reduced since 1983 with wastewater
treatment plant (WWTP) upgrades the
cumulative total amount of pollutants still
exceeds the assimilative capacity of the LMR
on the upper river. Signs of stress are evident
in higher rates of deformities, fin erosion,
lesions, and external tumors, known as DELT
anomalies in fish; and high soluble reactive
phosphorus (SRP) in the river segments
dominated by WWTP effluents (OEPA, 1995).
Data
This study analyzed hydrographic, land uses
and water-quality data from various sources
Table 1.
Data and data sources
Data type Data collection Data
time source
Water chemistry 1992–1996 USEPA
Fish (IBI) 1993 OEPA
Macroinvertebrate (ICI) 1993 OEPA
River habitat (QHEI) 1993 OEPA
IFD sites 1992 USEPA
TRI sites 1987–1995 USEPA
WWTP discharge points 1988 USEPA
1:100 000 scale 1994 USEPA
river network
Land use/land cover 1994 ODNR
(25-m resolution)
as shown in Table 1. The water chemistry
data were from STORET, a uniform data collection
and reporting system maintained by
USEPA, containing data describing surface
and ground water quality for North American
waterways (USEPA, 1992). Conventional pollutant
data for the watershed were retrieved
for 1992–1996 to ensure compatibility with
biological and habitat data (collected in 1993).
The indicators include dissolved oxygen

28 X. Wang
(DO), pH, total suspended solids (TSS530),
nitrogen-total ammonia (NH 3 ), total organic
carbon (TOC), and hardness. Those variables
were selected from commonly used indicators
based on the data availability in the
study area. Point pollution source data were
retrieved from three different sources. Discharges
from WWTPs, including municipal
plants and small, privately owned treatment
works, were retrieved from the 1988 USEPA
Needs Survey (USEPA, 1989). The Industrial
Facilities Discharge (IFD) sites were
obtained from a USEPA database, updated
in 1992, containing facility information on
industrial point source discharges to surface
waters (USEPA, 1998). Toxic Release Inventory
(TRI) sites were obtained from a USEPA
database maintaining facility information
for 1987–1995 TRI public data (USEPA,
1998).
Habitat, fish, and macroinvertebrate data
collected during an intensive 1993 LMR
survey were provided by the OEPA (Dyer
et al., 1998a). IBI was first developed from
12 metrics that reflected fish species richness
and composition, number and abundance of
indicator species, trophic organization and
function, reproductive behavior, fish abundance,
and condition of individual fish (Simon
and Lyons, 1995). Ten metrics were used to
construct ICI for invertebrates. The Qualitative
Habitat Evaluation Index (QHEI),
which was derived from six metrics, provided
a multi-parameter physical habitat
status of rivers and riparian areas (Rankin,
1989).
The land-use/land-cover data, obtained
from ODNR, were extracted from the Ohio
1994 statewide land-cover inventory. The
inventory was produced from Thematic Mapper
data acquired in September and October
1994 at a 25-m resolution. The data were
classified into seven general land-cover categories:
urban, agriculture, shrub, wooded,
open water, non-forested wetlands, and barren.
The digital hydrographic data were
based on USEPA’s reach file version 3,
RF3, a hydrological database of the surface
waters of the US in ARC/INFO line coverage
format. The database contains more
than 3Ð2 million records encompassing all
US streams (e.g. unnamed rivers and headwaters)
at a scale of 1:100 000 (Dyer et al.,
1998a).
Spatial integration
Biological, chemical and habitat monitoring
sites rarely occurred at the exact same
locations. To relate data from these sites in
a spatially meaningful way, those sites were
associated to river segments spatially. The
rivers were first divided into segments in a
way that WWTP discharges and confluences
of major tributaries (generally greater than
the first order tributaries) were used to
separate two adjacent segments. Then each
segment was assigned a unique identifier.
The GIS spatial overlay functions were used
to connect the river segments to the water
quality monitoring sites based on the nearest
distance. The result was that each monitoring
site had a unique river segment number.
Those monitoring sites with the same river
segment number were treated as within the
same geographic unit. Detailed discussions of
river segmentation and overlay analysis can
be found in Dyer et al. (1998a,b).
Twenty-two catchments for river segments
near headwaters and with water quality monitoring
data were delineated and digitized in
referencing to the river network. Only the
headwater catchments were used in the landuse
analysis so that the catchments were
spatially independent to each other. Those
catchments were overlaid with the land use
data to derive land use make-up for each
catchment, using the ArcView Spatial Analyst
Extension. The area and percentage of
land uses were calculated for each catchment.
Figure 2 displays the catchments and
land use compositions. Mean water quality
data values were calculated from multiple
monitoring sites in the same catchment.
Two classification schemes were used to
group the water quality monitoring sites.
The first scheme separated the monitoring
sites into two groups according to their
location to WWTPs. The first group included
those sites that were in the river segments
upstream from WWTPs and the other group
included those sites that were in the river
segments downstream from WWTPs. The
second scheme separated the monitoring
sites according to their proximity to point
sources and urban land. The first group
included those sites that were either located
in high human impact areas, including river
segments in urbanized area or immediately

Water-quality and land-use planning 29
N
10 0 10
kilometers
Figure 2. Land-use composition in selected catchments. Land-use composition: Urban ; agriculture ;
shrub scrub ; wooded . Other types of land use, not shown on the figure due to their small percentages,
are: open water, non-forested wetlands and barren. Little Miami River (. . .); watershed boundary (—).

30 X. Wang
downstream from a point pollution source,
which could be a WWTP, an IFD or a TRI
site. All other monitoring sites were included
in the second group, which represents the low
human impact areas.
Statistical analysis
Several statistical analyses were used to
analyze the spatial distribution patterns
of habitat, land uses, and water quality
measured with water chemistry, Fish (IBI),
and Macroinvertebrate (ICI) indicators, in
the study area. First, measures from the
sites that were immediate upstream from
WWTPs were compared with measures from
sites that were immediate downstream from
the same WWTPs with a paired t-test method
to test the hypothesis that water quality
decreased below WWTP discharge points.
Further, an independent two-sample t-test
was performed to test the hypothesis that
water qualities of river segments in high
human impact areas were worse than that of
river segments in low human impact areas.
Finally, biological, habitat, and water
chemistry monitoring values from the river
segments and land use distribution within
corresponding catchment were analyzed
using the Pearson’s correlation to reveal
any possible relationships between biological
indicators and land uses and riparian habitat
indicator. Multiple regression was then used
to determine the principle driving forces
for biotic integrity within the LMR (Dyer
et al., 1998a). The purpose of the analysis
is to evaluate the strength of the impact
of land uses on the quality of receiving
waters. Several water-chemistry parameters
that had very small sample sets or were
dominated by detection limit were dropped
from the analysis.
Results and discussion
The results of this study are presented and
discussed from three aspects—the impact
of wastewater treatment plants, the spatial
patterns of river-water quality, and the
relationship between land uses in catchments
and water quality of the receiving water. The
importance of considering water quality in
land-use planning is discussed based upon
the findings from this study.
Impact of wastewater treatment
plants
Table 2 displays the results from a paired
t-test of the IBI, ICI, and QHEI in reference
to WWTP discharge points. The IBI
measurement from the closest sites to the
discharge points demonstrated a statistically
significant decrease of water quality downstream
from WWTP discharges. Although
both ICI and QHEI demonstrated similar
trend, the change was not statistically significant.
This implies that the water quality
may not change significantly below and above
WWTP discharge points. The lack of strong
impact may be attributed to the better municipal
WWTP practices (OEPA, 1995). The
result concurs with findings by others that
improved management of sewage reduced the
impact on receiving waters (Wichert, 1995;
Frenzel, 1990). The result also suggests a further
study to analyze the discrepancy of the
sensitivities of fish indicators (IBI) and invertebrate
indicators (ICI) to WWTP discharges.
Spatial patterns of water quality
A visual examination of spatial distributions
of the urban land use shows that there
are two major urban areas within the LMR
watershed. One is near the basin outlet
at the lower left portion of the watershed
and another is at upper left. In addition,
there are a few smaller settlements scattering
within the watershed (Figure 3). It
is noticed that various types of point pollution
sources are also concentrated in or near
the more urbanized areas. A t-test of the
Table 2. Matched-pair t-test of water quality from
upstream and downstream of WWTPs
Variable Paired differences a t df Significance
(1-tailed)
Mean SD
IBI 4Ð769 9Ð471 1Ð816 12 0Ð0472 Ł
ICI 1Ð000 7Ð886 0Ð439 11 0Ð3345
QHEI 1Ð875 20Ð190 0Ð256 11 0Ð3770
a Paired difference is calculated as downstream monitoring
value minus upstream monitoring value for the same
WWTP.

Water-quality and land-use planning 31
N
*
*
* * *
* *
* *
*
*
* *
*
* *
*
*
*
**
*
* ** * *
**
*
*
*
*
*
* *
* *
* * *
* **
* *
* **
**
*
* *
* * * ** * *
* *
*
*
*
*
*
*
*
*
*
*
*
*
**
*
**
*
10 0 10
kilometers
Figure 3. Urban land and point pollution sources. Industrial facility discharge sites (Ł); wastewatertreatment
plants ( ); toxic release inventory sites ( ); Little Miami River (. . .); watershed boundary ( ).
mean monitoring values from the sites in the
high and low human impact areas was performed
upon the three indicators (Table 3).
Both IBI and ICI values demonstrated significantly
lower values in high human impact
areas. It was interesting to note that habitats
also showed lower quality in those areas, as
indicated by low QHEI scores. These results
imply that the biological integrity in rivers
flowing through high human impact areas
tend to be lower.
Land uses and water quality of the
receiving waters
Among the 22 catchments, urban land
percentages varied from 1% to 58% and

32 X. Wang
Table 3. Independent t-test on the water monitoring
data in LMR watershed
Para- High human Low human t p a
meter impact area impact area
Mean N Mean N
IBI 33Ð27 77 44Ð50 68 8Ð625 5Ð55E-15 b
ICI 34Ð94 36 43Ð77 35 3Ð288 0Ð0008 b
QHEI 62Ð45 73 73Ð77 61 5Ð869 1Ð67E-08 b
a Significance level or the less-than and equal-to probability
of the t value.
b Significant at the 0Ð05 level.
agricultural-land percentages varied between
12% and 95% (Figure 2). The IBI and ICI
have similar relationships to habitat quality
(QHEI) and land uses although the levels
of significance vary (Table 4). These biological
indicators are negatively related to
the percentages of urban land use and positively
related to agricultural land use. They
also are positively related to habitat quality.
The correlation analysis showed that at
0Ð05 significant level the IBI scores were
significantly correlated with percentage of
urban land use ( 0Ð59) and agricultural
land use (0Ð53). IBI was also positively correlated
with QHEI (0Ð67). The correlations
between ICI and QHEI and land uses were
not statistically significant. The results suggest
that IBI may be a more sensitive to
land-use composition and riparian-habitat
quality.
Table 4. Pearson product movement correlation
coefficients
Land use and habitat IBI ICI
Urban
Pearson correlation 0Ð59 a 0Ð22
Significance (2-tailed) 0Ð00 0Ð40
Sample size 22 16
Agriculture
Pearson correlation 0Ð53 a 0Ð30
Significance (2-tailed) 0Ð01 0Ð26
Sample size 22 16
Wooded
Pearson correlation 0Ð27 a 0Ð28
Significance (2-tailed) 0Ð23 0Ð30
Sample size 22 16
QHEI
Pearson correlation 0Ð67 a 0Ð41
Significance (2-tailed) 0Ð00 0Ð11
Sample size 22 16
a Correlation is significant at the 0Ð05 level (2-tailed).
In a previous study Dyer et al. (1998a)
applied a multivariate forward stepwise
regression model to determine the relative
importance of water chemistry and habitat
on biological indicators in the Little Miami
River watershed. Their study concluded that
the habitat quality was primarily responsible
for the biological integrity of receiving waters
in the watershed. A similar regression analysis
was conducted in this study on the 22
selected catchments. Percentages of urban
and wooded land uses by catchment were
included in the multiple regression analysis,
in addition to the habitat and water chemistry
indicators used in Dyer et al. (1998a).
The percentage of agricultural land was not
included because it was highly correlated
with the percentage of urban land. Other
independent variables were the six water
chemistry variables—dissolved oxygen, pH,
total suspended solids, nitrogen-total ammonia,
total organic carbon, and hardness, and
QHEI. The dependent variables were IBI and
ICI, respectively. Figure 4 shows the scatter
plots of the predicted values against measured
values for IBI and ICI, respectively.
The results shown in Table 5 indicate that
the land-use components within the catchments
could be major predictors for biotic
integrity. The percentage of urban land was
the second strongest predictor for both IBI
and ICI. The negative signs of those coefficients
indicate that as the intensity of human
activities increase there is a tendency that the
biological integrity of the rivers decreases.
The percentage of wooded land was the third
strongest predictor for IBI. The positive sign
of the coefficient shows that higher river biological
quality may be expected in areas of
less intensity of human impact. When the
results for the two dependent variables, it
appears that the independent variables can
explain IBI better than ICI.
Water quality consideration in
land-use planning
This study exhibits the complexity of water
quality indicators and their spatial distribution.
Such complexity implies that different
indicators often reflect different aspects of a
water body and the status of water quality
may be affected by many factors in different
ways. Although water chemistry in the Little

Water-quality and land-use planning 33
Regression adjusted (press) predicted value
Regression adjusted (press) predicted value
60
50
40
30
20
10
50
40
30
20
10
0
(a)
(b)
10
20
30 40
Measured value
20 30 40
Measured value
Figure 4. Comparison of predicted and measured
biological indicator values. (a) Dependent variable:
IBI; (b) dependent variable: ICI.
Miami River was at good condition (several
of the water chemistry variables were at
or below detection limit, which might have
contributed to the fewer data available for
the analysis), biotic indicators have picked
up some effects of human activities on the
receiving water. The t-test showed that urban
land and point sources (WWTPs, IFDs, and
TRIs) together might explain the lower biotic
quality throughout the watershed. This finding
confirms that one of the greatest causes
50
50
60
60
of water-quality problem derives from urban
land use as a result of the increasing intensity
of human activities. Pollution has resulted in
loss of species diversity within rivers (Haycock
and Muscutt, 1995).
The hydrological relationship between
water systems and the land requires
coordination between the water management
and land management fields. Once the
land–water relationship is identified, it
leads to the need of protecting water
quality through proper land-use planning by
identifying cost-effective pollution prevention
and pollution correction approaches that
can address all the sources of pollution
in a comprehensive way. To take such
challenge, it is necessary to look into
water-quality management and land-useplanning
practices and draw the connection
between the two. By tradition, waterquality
management and land-use planning
are implemented by different agencies
with different objectives. The purpose of
water-quality management is to maintain
and improve ambient water quality,
which requires designation of water
usage, establishment of criteria to protect
designated uses, and development of waterquality
management plans accordingly.
The objective of land-use planning is to
maximize the uses of land by humans
while minimizing the negative impact to
humans’ health and welfare. Land-use
planning, after systematically analyzing
different alternatives and the need for
land use changes, determines future land
uses, improves physical conditions for the
planned land uses, and manages activities
associated with the planned land uses (van
Lier, 1998). In practice, land-use planning
is often fragmented temporally and spatially
since most land-use plan is often produced
for area within a political boundary and
Table 5.
Results of forward stepwise multiple regression analysis
Dependent variable IBI ICI
Adjusted R 2 0Ð934 0Ð773
Coefficient
Constant 16Ð823 2Ð345
Predictor 1 QHEI 0Ð761 Hardness 0Ð058
Predictor 2 % of Urban land 45Ð078 % of Urban land 81Ð472
Predictor 3 % of Wooded land 35Ð194 Dissolved oxygen 3Ð793
Use probability of F less than or equal to 0Ð1 for inclusion of the independent variables (predictors)
and the coefficients are different from zero at 0Ð5 significance level.

34 X. Wang
only to serve the community which adopt
the plan. In the United States, land-use
planning is implemented at local community
level (municipal or county) (Thomas and
Furuseth, 1997) and consequently non-local
interests are not considered equally in landuse
planning decision-making. For example,
typical land-use suitability and feasibility
analyses often are limited to the proposed
property and immediately surrounded areas.
Water-quality issue is usually not sufficiently
studied in land-use planning.
The impact of urban land uses on river
water quality demonstrated in this study
suggests that the known land–water relationship
is significant enough for planners
and decision-makers to pay proper attention
to water-quality issues in evaluating
plans and facilitating collaborations. Achieving
the sustainable management of water
and land resources could be a major consideration
in exploring planning alternatives
within a watershed. After realizing the
water-quality problems related to non-point
sources and the loss of aquatic habitat, the
US EPA has been promoting an ecologicalbased
watershed protection approach (WPA)
(Brady, 1996). The WPA delineates a geographic
area based on its natural characteristics—a
watershed—and the stakeholders
whose activities are on water or land within
the watershed are involved in defining problems,
set priorities, and implement solutions
(Davenport et al., 1996). The LMR study
shows that the WWTPs alone may not significantly
affect the water quality while the
combined affect from point sources (WWTPs,
TRIs, IFDs) and non-point sources (urban
land) can be reflected in the water-quality
data. At present, only point sources are regulated
by environmental agencies such as
OEPA in LMR watershed while non-point
sources are unregulated. This study result
shows that such management may not be
effective in water quality protection. The
finding reinforces the notion that management
of point and non-point sources should
be coordinated. Such effort involves all levels
of government, other agencies and stakeholders
in a structured and focused process
since a sustainable community is interconnected
with surrounding communities and
the sustainability of a larger region is supported
by the collaboration of these communities
(Thomas and Furuseth, 1997). Proper
land-use planning within a watershed can
protect water quality and reach economic
goals. Although watersheds are increasingly
viewed as appropriate natural spatial unit for
planning and for sustainable water resources
management, watersheds have not received
as much attention in land-use planning field
as that in the biological and environmental
studies. This may be attributed to the nature
of traditional planning practice. Watersheds
are often divided into areas that are under
different planning and political jurisdictions
and the coordination among them is often
minimal. With more studies demonstrating
that the effects of human activities can and
do cross political boundaries the development
and implementation of water-quality-based
watershed land-use plans should be viewed
as an integrated and holistic approach.
The LMR study demonstrates several evidences
that call for integration of waterquality
management and land-use planning
to aim at water uses in a manner that will
maximize the socio-economic benefits to the
society without jeopardizing the balance of
the resource-related ecosystems. Although
water chemistry in the LMR watershed was
at good condition, biotic indicators have
picked up the effect of human activities on
the water quality. Such effect is a combination
of point and non-point sources, which are
connected with land uses in the watershed,
and the riparian habitat quality. The relationship
between water quality of receiving
rivers and land uses in a watershed indicates
that increasing population pressure in
a watershed is resulting in increasing loads
of nutrients and other pollutants which may
cause severe degradation of water quality
and consequent use impairments of the water
bodies. The integration of water-quality management
and land-use planning can promote
protecting the biotic quality and habitat
health and preventing pollution from happening,
which serves the purpose of protecting
water quality and maintaining ecologically
and economically healthy land development.
The study also demonstrates that the river
biological integrity is strongly related to
the habitat health (Tables 4 and 5). This
linkage suggests that the goal of protecting
water quality through land-use planning
can and should be achieved through habitat
protection. Maintaining a healthy habitat can
help to improve water quality and promote

Water-quality and land-use planning 35
biodiversity and preserve landscape features
and the aesthetic appeal of the watershed.
A good example of such integration is to
develop riverside corridors that can have
many benefits such as protecting water
quality, enhancing biological diversity and
minimizing soil erosion.
As water quality and land-use data become
more accessible, planners and policy-makers
at different levels should bring stakeholders
together to substantially increase the health
of the environment by identifying sources of
the problems, understanding the relationship
between the sources and consequences, and
searching for solutions to these problems.
This study shows that such effort can be at a
local level, such as protect and improve riparian
habitat through a variety of planning
practices such as vegetation buffers along
rivers and better management of discharges
into the river. The protection of river also
extends to land uses in the entire watershed,
which requires a more regional collaboration.
Acknowledgements
The author thanks Scott Dyer and Charlotte White
who provided data and initiated the study, and the
anonymous reviewers who contributed through
discussions and comments for this manuscript.
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