Effects of urbanization and forest fragmentation on water quality … Rachel Riemann USFS-FIA Karen...
Transcript of Effects of urbanization and forest fragmentation on water quality … Rachel Riemann USFS-FIA Karen...
![Page 1: Effects of urbanization and forest fragmentation on water quality … Rachel Riemann USFS-FIA Karen Murray USGS-NAWQA.](https://reader035.fdocuments.in/reader035/viewer/2022062515/56649c9b5503460f94958cba/html5/thumbnails/1.jpg)
Effects of urbanization and forest fragmentation on water quality …
Rachel Riemann USFS-FIA
Karen Murray USGS-NAWQA
![Page 2: Effects of urbanization and forest fragmentation on water quality … Rachel Riemann USFS-FIA Karen Murray USGS-NAWQA.](https://reader035.fdocuments.in/reader035/viewer/2022062515/56649c9b5503460f94958cba/html5/thumbnails/2.jpg)
Opportunity for collaboration
In this study we take advantage of current USGS-NAWQA 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.
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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?
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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
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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
• Piedmont ecoregion– Including Philadelphia – Trenton corridor
– Rapidly transitioning from agriculture to suburban
Appalachian Plateau
Valley and Ridge
Piedmont
Coastal Plain
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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.
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% Urban - photointerp.
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Predictor data used
NLCD’92 wasn’t sufficiently accurate to do the job, particularly in the less urbanized Poconos region
%urban (source: NLCD’92)
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Predictor data used
• From photointerpretation of land use and land cover from digital aerial photography (1999-2000; 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)
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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
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0 5 10 15 20
flatlbusbrodvandhaycsawkpidcfrenebbrlhgowbbrtoby
marspick
macoraympigeridl
tomsdingebrcpinevall
crumlnesdarfmill
shabwyom
tacocobb
Road density (road miles/ sq. mi. basin)
Piedmont sitesPoconos sites
Bar graph-fixed
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Similar %forest and same amount of urban development, but different % forest in buffer, and different %C/I
Illus maps
East Branch Red Clay
East Branch Brandywine
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Similar %forest and amount of development, but a different distribution of land uses (COR, AI, and forest patch size covariance)
Illus maps
DingmansHay
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• Macroinvertebrates• Algae
Field data collected
• Habitat & geomorphology
• Nutrients, ions
• Pesticides in water
• Discharge (instantaneous)
• Temperature
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Primary responses related to urbanization (rdden)
What are the primary biological, physical, and chemical responses that are related to urbanization?
EP
T r
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Road density in basin (mi/mi2)
• Loss of sensitive macroinvertebrates
Road density in basin (mi/mi2)
Hab
itat Q
ualit
y In
dex
• Decrease in habitat quality
Chl
orid
e (m
g/l)
Road density in basin (mi/mi2)
• Increase in chloride, sulfate, other major ions
log1
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tal N
(m
g/l)
Road density in basin (mi/mi2)
• Increase in nutrient concentrations
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Road density in basin (mi/mi2)
Road density in basin (mi/mi2)
What are the primary biological, physical, and chemical responses that are related to urbanization?
• Increase in Pesticide Toxicity Index• Increased variety and amounts of pesticides detected
(especially insecticides) • Increased potential toxicity of streamwater to fish and
invertebrates
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Ecosystem responses…
Results--What are the changes we see?
• Loss of sensitive macroinvertebrates
• Decrease in habitat quality
• Increase in chloride, sulfate, other major ions
• Increase in nutrient concentrations
• Increase in Pesticide Toxicity Index
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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
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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 variables
* Buffer zone = 100m on either side of the stream
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Landscape indices
Landscape index
Sensitive invertebrates
Chloride conc.
Pesticide toxicity
Habitat quality
Mean patch size - forest + - - +
Avg patch perimeter-forest + - - +
Aggregation index - forest + - -
Centroid connectivity - forest + - -
Edge - urban - +
Avg patch perimeter - urban +
Distribution/frag measures
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Can we combine some of these landscape factors to develop models of stream ecosystem response?
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Multiple linear regression –Invertebrate community structure*
Variable added
to model
Model R-Square
(p<0.01)% Forest in basin (+) 0.77
% Commercial in basin (-) 0.82
% Urban in buffer (-) 0.86
*ordination site scores
MLR - invertebrates
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Multiple linear regression –Total nitrogen (spring sample)
Landscape variable
added to model
Model R-Square
% Forest in basin (-) 0.68
Relative contagion (-) 0.76
% commercial/industrial in basin (+)
0.81
MLR-total nitrogen (spring)
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What is the form of the response?
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…
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% Urban - NLCD 1992
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% Urban - photointerp.
EP
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% Urban - NLCD 2000
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% Urban - NLCD 1992
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% Urban - photointerp.
log1
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“Correcting” the NLCD92 dataset
NLCD’92
…with roads overlaid on top
NLCD’92 – ‘corrected’ using local road density
example in to the Poconos area…
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Differences-plotsBasin stats Buffer stats
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Where it helps and where it doesn’t…
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% Urban - Corrected NLCD 1992
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% Urban - NLCD 1992
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% Urban - photointerp.
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Helps:
–%urban land in basin
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% Buffer as urban -corrected NLCD 1992
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% Buffer as urban - NLCD 1992
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% Buffer as urban - photointerp.
Not much help:
–%urban land in buffer
Where it helps and where it doesn’t…
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Differences between them• Description, comparison maps and comparison plots
Photointerpretedland use (1999)
NLCD’92(note missing development)
NLCD2000(note land cover focus)
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Looking at NLCD2000…
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)
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Concluding thoughts…
– %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).
• Landscape variables most related to stream ecosystem response
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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
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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.