Effects of urbanization and forest
fragmentation on water quality …
Rachel Riemann USFS-FIA
Karen Murray USGS-NAWQA
Opportunity for collaboration
In this study we take advantage of current USGSNAWQA
water quality monitoring
efforts and link it to
USFS-FIA’s current
investigations into monitoring forest
fragmentation and urbanization
-- in order to better understand the relations
between the two.
Problem
We already know that urbanization has been
linked to water quality in other studies
But, what aspects of urbanization and/or forest
fragmentation are most highly correlated with the
biological, chemical, and physical responses
observed in streams?
a combination of interests…
• USGS-NAWQA
– Improve understanding of the components impacting water
quality in order to better provide management guidelines for
preventing or minimizing degradation in the face of
development pressure
– Improve understanding of the forms and/or thresholds of that
impact
• USFS-FIA
– Identify the components of frag/urban that are most related to
observed changes in water quality,
– Develop methods to monitor these relevant parameters of
frag/urbanization with sufficient accuracy over large areas
Two rapidly urbanizing areas:
• Appalachian ecoregion, especially the
Pocono Mountains area
– Fastest growing counties in Pennsylvania
– Second home and primary home
development
– Transitioning from forested to suburban
Appalachian Plateau
Valley and Ridge
Piedmont
• Piedmont ecoregion
– Including Philadelphia – Trenton corridor
– Rapidly transitioning from agriculture to
suburban
Coastal Plain
Objectives
• Identify which management-relevant landscape
characteristics are most related to stream water quality
and ecological health.
• Describe the forms of these relationships.
• Determine the influence of landscape data source (on
interpretation/findings).
• If necessary develop corrections or recommendations
for use of those broad-area datasets currently available.
log10 PTI
-3
-4
-5
20
40
60
80
% Urban - photointerp.
Predictor data used
%urban (source: NLCD’92)
NLCD’92 wasn’t sufficiently accurate to do the
job, particularly in the less urbanized Poconos
region
Predictor data used
• From photointerpretation of land use and land
cover from digital aerial photography (19992000; some CIR, some B&W)
– Land use polygons
– land cover data recorded for each urban
developed land use class (% tree, grass,
house, road)
• From Census Bureau data (2000)
– Population
– House density
– Roads and road density (2000 TIGER data)
Site selection
• Minimize point sources
• Minimize natural variation –
- basin size all 20-60 mi2
- slope - all upland, riffle/pool sites
• Accessible for sampling during both low and high flows
• Selected representative sampling reach 150-300m long
33 sites
cobb
taco
wyom
shab
mill
darf
lnes
crum
vall
pine
ebrc
ding
toms
ridl
pige
raym
maco
pick
mars
toby
wbbr
lhgo
ebbr
fren
pidc
sawk
hayc
vand
brod
lbus
flat
Station
Piedmont sites
Poconos sites
0
5
10
15
Road density (road miles/ sq. mi. basin)
20
East Branch Brandywine
East Branch
Red Clay
Similar %forest and same amount of urban development, but
different % forest in buffer, and different %C/I
Dingmans
Hay
Similar %forest and amount of development, but a different
distribution of land uses (COR, AI, and forest patch size covariance)
Field data collected
• Macroinvertebrates
• Algae
• Habitat & geomorphology
• Nutrients, ions
• Pesticides in water
• Discharge (instantaneous)
• Temperature
EPT richness
Chloride (mg/l)
What are the primary biological, physical, and chemical
responses that are related to urbanization?
Road density in basin (mi/mi2)
• Increase in chloride, sulfate, other major ions
log10 total N (mg/l)
Habitat Quality Index
• Loss of sensitive macroinvertebrates
Road density in basin (mi/mi2)
Road density in basin (mi/mi2)
• Increase in nutrient concentrations
Road density in basin (mi/mi2)
• Decrease in habitat quality
log10 Pesticide Toxicity
Index
What are the primary biological, physical, and chemical
responses that are related to urbanization?
Road density in basin (mi/mi2)
Road density in basin (mi/mi2)
• Increase in Pesticide Toxicity Index
• Increased variety and amounts of pesticides detected
(especially insecticides)
• Increased potential toxicity of streamwater to fish and
invertebrates
Ecosystem responses…
• Loss of sensitive macroinvertebrates
• Increase in chloride, sulfate, other major ions
• Increase in nutrient concentrations
• Increase in Pesticide Toxicity Index
• Decrease in habitat quality
To what specific landscape characteristics (or
combination of characteristics) are these
responses related?
• basin-wide land use
• buffer zone land use
• fragmentation indices for basin
Buffer-zone variables
“Buffer – zone”
landscape variable
Sensitive
invertebrates
Chloride
conc.
Pesticide
toxicity
Habitat
quality
Forested %
+
-
-
+
Multi-family residential %
-
+
+
-
Commercial-industrial %
-
+
+
-
Impervious %
-
+
+
-
Urban %
-
+
+
-
* Buffer zone = 100m on either side of the stream
Distribution/frag measures
Sensitive
invertebrates
Chloride
conc.
Pesticide
toxicity
Habitat
quality
Mean patch size - forest
+
-
-
+
Avg patch perimeter-forest
+
-
-
+
Aggregation index - forest
+
-
-
Centroid connectivity - forest
+
-
-
Edge - urban
-
Landscape index
Avg patch perimeter - urban
+
+
Can we combine some of these landscape
factors to develop models of stream
ecosystem response?
Multiple linear regression –
Invertebrate community structure*
Variable added
to model
% Forest in basin (+)
Model R-Square
(p<0.01)
0.77
% Commercial in basin (-)
0.82
% Urban in buffer (-)
0.86
*ordination site scores
Multiple linear regression –
Total nitrogen (spring sample)
Landscape variable
added to model
Model RSquare
% Forest in basin (-)
0.68
Relative contagion (-)
0.76
% commercial/industrial in
basin (+)
0.81
What is the form of the response?
And how does data source affect observed patterns?
• NLCD’92
– Currently available over entire US
• NLCD2000
– Currently only exists in pilot areas.
Expected to have US-wide coverage in
the next 5 years or so…
EPT richness
25
20
15
10
5
20
40
60
80
EPT richness
% Urban - photointerp.
25
20
15
10
5
20
40
60
80
% Urban - NLCD 1992
log10 PTI
-3
-4
-5
20
40
60
80
log10 PTI
log10 PTI
% Urban - photointerp.
-3
-4
-5
-3
-4
-5
20
40
60
80
% Urban - NLCD 1992
20
40
60
80
% Urban - NLCD 2000
“Correcting” the NLCD92 dataset
example in to the Poconos area…
NLCD’92
…with roads overlaid on top
NLCD’92 – ‘corrected’ using local road density
Differences-plots
Basin stats
Buffer stats
100
120
90
100
80
80
%total urban
60
%residential
%ag
40
NLCD92
nlcd92
%forest
%dev
20
70
%forest
60
%total urban
50
%residential
40
%ag
30
%dev
20
10
0
0
20
40
60
80
0
100
0
10
20
30
40
50
60
70
80
90
100
PI
100
100
90
80
%forest
%total urban
60
%residential
40
%ag
%dev
20
NLCD92-corrected (circ7)
nlcd92-corrected (circ7)
pi
80
70
%forest
60
%total urban
50
%residential
40
%ag
30
%dev
20
10
0
0
20
40
60
80
100
0
0
pi
10
20
30
40
50
PI
60
70
80
90
100
Where it helps and where it doesn’t…
Helps:
–%urban land in basin
EPT richness
25
20
15
10
5
20
40
60
80
25
EPT richness
EPT richness
% Urban - photointerp.
20
15
10
5
25
20
15
10
5
20
40
60
80
% Urban - NLCD 1992
20
40
60
80
% Urban - Corrected NLCD 1992
EPT richness
Where it helps and where it doesn’t…
Not much help:
–%urban land in buffer
25
20
15
10
5
20
40
60
80
% Buffer as urban - photointerp.
EPT richness
EPT richness
25
20
15
10
5
25
20
15
10
5
20
40
60
80
% Buffer as urban - NLCD 1992
20
40
60
80
% Buffer as urban corrected NLCD 1992
Photointerpreted
land use (1999)
NLCD2000
(note land cover focus)
NLCD’92
(note missing
development)
Being a land cover product, NLCD2000 urban developed land uses are more related to
impervious surface than the entire developed area.
And, areas that are sparsely developed, have small house footprints, and/or have trees
overshadowing roads or buildings may still contain only a few ‘developed’ pixels in the
NLCD2000 dataset within a background of grass (or forest)
Concluding thoughts…
• Landscape variables most related to stream
ecosystem response
– %Forest in the basin (and its close opposite--%developed)
– The type of developed land in the basin (e.g. C/I)
– Distribution of land uses within the basin can be a factor
• Amount of forest or urban in the buffer
• COR, AI-Forest, diversity of forest patch sizes
– Land cover
• % impervious
• And, although the data wasn’t fully analyzed, there was some
evidence suggesting that the land cover of developed land uses
may be a factor as well (e.g. forest vs. grass covered
residential).
Concluding thoughts
• Data source
– You sometimes need the detailed land use/land cover
information to find out what’s really going on
– And you need an understanding of its relationship to
the broadly available datasets for extrapolation over
large areas
• Be very careful using threshold values derived
using one land use data source and applying
them to another
Concluding thoughts
• The cooperative effort provided a unique
opportunity
– To link forest and water studies to expand
ecosystem knowledge
– To investigate the linkage between a process-level
study establishing relationships between factors
and broad scale methods for scaling the results up
to an entire region.