JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION
Vol. 45, No. 2
AMERICAN WATER RESOURCES ASSOCIATION
April 2009
LINKING HYDROLOGIC ALTERATION TO BIOLOGICAL IMPAIRMENT IN
URBANIZING STREAMS OF THE PUGET LOWLAND, WASHINGTON, USA1
Curtis L. DeGasperi, Hans B. Berge, Kelly R. Whiting, Jeff J. Burkey, Jan L. Cassin, and Robert R. Fuerstenberg2
ABSTRACT: We used a retrospective approach to identify hydrologic metrics with the greatest potential for ecological relevance for use as resource management tools (i.e., hydrologic indicators) in rapidly urbanizing basins of the
Puget Lowland. We proposed four criteria for identifying useful hydrologic indicators: (1) sensitive to urbanization
consistent with expected hydrologic response, (2) demonstrate statistically significant trends in urbanizing basins
(and not in undeveloped basins), (3) be correlated with measures of biological response to urbanization, and (4) be
relatively insensitive to potentially confounding variables like basin area. Data utilized in the analysis included
gauged flow and benthic macroinvertebrate data collected at 16 locations in 11 King County stream basins. Fifteen
hydrologic metrics were calculated from daily average flow data and the Pacific Northwest Benthic Index of Biological Integrity (B-IBI) was used to represent the gradient of response of stream macroinvertebrates to urbanization.
Urbanization was represented by percent Total Impervious Area (%TIA) and percent urban land cover (%Urban).
We found eight hydrologic metrics that were significantly correlated with B-IBI scores (Low Pulse Count and
Duration; High Pulse Count, Duration, and Range; Flow Reversals, TQmean, and R-B Index). Although there
appeared to be a great deal of redundancy among these metrics with respect to their response to urbanization, only
two of the metrics tested – High Pulse Count and High Pulse Range – best met all four criteria we established for
selecting hydrologic indicators. The increase in these high pulse metrics with respect to urbanization is the result
of an increase in winter high pulses and the occurrence of high pulse events during summer (increasing the frequency and range of high pulses), when practically none would have occurred prior to development. We performed
an initial evaluation of the usefulness of our hydrologic indicators by calculating and comparing hydrologic metrics
derived from continuous hydrologic simulations of selected basin management alternatives for Miller Creek, one of
the most highly urbanized basins used in our study. We found that the preferred basin management alternative
appeared to be effective in restoring some flow metrics close to simulated fully forested conditions (e.g., TQmean),
but less effective in restoring other metrics such as High Pulse Count and Range. If future research continues to
support our hypothesis that the flow regime, particularly High Pulse Count and Range, is an important control of
biotic integrity in Puget Lowland streams, it would have significant implications for stormwater management.
(KEY TERMS: benthic macroinvertebrates; environmental indicators, hydrologic metrics; index of biological
integrity; land use ⁄ land cover change; urbanization; urban streams; watershed management.)
DeGasperi, Curtis L., Hans B. Berge, Kelly R. Whiting, Jeff J. Burkey, Jan L. Cassin, and Robert R. Fuerstenberg, 2009. Linking Hydrologic Alteration to Biological Impairment in Urbanizing Streams of the Puget
Lowland, Washington, USA. Journal of the American Water Resources Association (JAWRA) 45(2):512-533.
DOI: 10.1111/j.1752-1688.2009.00306.x
1
Paper No. JAWRA-07-0162-P of the Journal of the American Water Resources Association (JAWRA). Received November 28, 2007;
accepted September 22, 2008. ª 2009 American Water Resources Association. No claim to original U.S. government works. Discussions are
open until October 1, 2009.
2
Respectively, Lead Hydrologist (DeGasperi), Senior Ecologist (Berge and Fuerstenberg), Hydrologist II (Burkey), King County Department of Natural Resources and Parks; Engineer IV (Whiting), King County Department of Transportation, MS KSC-0600, 201 S. Jackson
Street, Seattle, Washington 98104; and Ecologist (Cassin), Parametrix, Inc., 411 108th Avenue NE, Suite 1800, Bellevue, Washington 98004
(E-Mail ⁄ DeGasperi: curtis.degasperi@kingcounty.gov).
Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.
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INTRODUCTION
In highly urbanized lowland areas surrounding
Puget Sound, Washington (Figure 1), forest cover has
been reduced to a few patches along highly altered
stream courses that were not incorporated into the
subterranean stormwater conveyance system. In the
suburban and rural fringe between these metropolitan areas and the national and state forest lands concentrated along the western flanks of the Cascade
Mountains, private forest lands previously managed
for timber are being converted to other uses as a consequence of urban sprawl (McClinton and Lassiter,
2002; Alig et al., 2003; Northwest Environmental
Forum, 2006; Plantinga et al., 2007). Significant
losses of forest are projected to continue with losses
concentrated along the fringes of growing metropolitan areas and major transportation corridors (Alig
et al., 2003, 2004; Plantinga et al., 2007).
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The general phenomenon of conversion of fringe
rural resource lands to other uses (primarily suburban development and transportation) is mirrored in
metropolitan areas across the globe as the human
population grows and is concentrated in cities and
expands into the suburban fringes (Alig et al., 2004).
A complex interaction of socioeconomic factors drives
urban sprawl that includes extension and expansion
of urban services to rural areas (e.g., sewer, water,
and roads), low timber ⁄ agricultural resource values
relative to conversion to suburban uses, and lack of
consideration of the value of the ecosystem services
that healthy forests provide (Alberti et al., 2003; Alig
et al., 2004; Alberti, 2005).
The loss of forest cover and development in the
Puget Lowland increases winter peak flows and
decreases winter base flows as interception and infiltration of rainfall is reduced and runoff from compacted soils and impervious cover is more quickly
routed to receiving streams via engineered conveyance
FIGURE 1. Map of Study Area Showing the 16 Sub-Basins and Urban vs. Forested Land Cover.
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networks (Booth, 1991; Booth and Jackson, 1997; Konrad and Booth, 2005). In the absence of mitigation
measures, increased runoff typically results in
increased frequency and magnitude of flooding and
channel erosion (Booth and Jackson, 1997; Konrad
et al., 2005). Runoff from impervious surfaces also
typically delivers greater amounts of nutrients, sediments, fecal indicator bacteria, and chemical contaminants (Hatt et al., 2004; Carle et al., 2005). In addition
to these hydrologic and water quality effects, forest
clearing and development result in direct and mostly
irreversible loss of habitat, displacement ⁄ extirpation
of native species, and ecosystem fragmentation and
degradation (Vitousek et al., 1997; McKinney, 2002).
In the Puget Sound region, the most visible loss of
aquatic species partially attributable to urban development has been the reduction and extinction of
native salmon populations, and changes in the species
assemblages of urbanized streams (Nehlsen et al.,
1991; Frissel, 1993; Doberstein et al., 2000; Matzen
and Berge, 2008).
A variety of research efforts conducted in the
Puget Lowlands of Washington have demonstrated a
statistical relationship between human development
of the landscape – most typically represented by percent Total Impervious Area (%TIA) – on benthic
invertebrate community structure – typically represented by the Pacific Northwest Benthic Index of Biological Integrity (B-IBI) (May et al., 1997; Morley and
Karr, 2002; Booth et al., 2004; Alberti et al., 2007).
However, the specific environmental changes causing
decreasing B-IBI scores with urbanization have not
been conclusively identified. Potential changes
include water quality impairment, habitat degradation, and hydrologic alteration and more specifically
include changes in channel morphology, streambed
material, nutrients, migration barriers, water temperature, and water chemistry (Konrad and Booth,
2005). Unfortunately, these variables are generally
correlated with each other: multiple, scale-dependent
mechanisms are at play; responses to stressors are
typically nonlinear; and there are difficulties associated with separating present-day from past effects
(Allan, 2004).
Nonetheless, a few authors have suggested that
hydrologic alteration is the primary cause of declining biological richness and B-IBI scores as basins
become urbanized (Wang et al., 2000; Morse et al.,
2003; Booth et al., 2004; Walsh, 2004; Konrad and
Booth, 2005; Walsh et al., 2005). The lack of strong
correlations between conventional water quality and
B-IBI except in the most highly urbanized streams
(May et al., 1997) lends further evidence (albeit circumstantial) to the predominant role hydrologic
change plays in declining biological health (Booth,
2005).
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Parallel to the increasing suspicion that hydrologic
change is a major driver of biological degradation in
streams is the recognition that native stream biota
are best adapted to the natural flow regime – the flow
regime typical of the millennia prior to significant
human alteration of the landscape (Richter et al.,
1996, 1997; Poff et al., 1997). Although the historical
flow regime was not without its inter- and intraannual disturbances, forest clearing and urbanization
in the Puget Lowlands over the last 150 years have
dramatically altered the historical flow regime, exacerbating disturbances during winter high flows and
introducing disturbances during late summer when
none typically occurred in the past (Booth, 1991; Konrad et al., 2005 – see Figure 2).
A host of hydrologic metrics have been developed
to provide quantitative measures of hydrologic
change between predisturbance and postdisturbance
conditions (e.g., Richter et al., 1997; Clausen and
Biggs, 2000). The difficulty lies in identifying hydrologic metrics that respond to urbanization and can
also be shown to be biologically relevant (Poff et al.,
1997; Konrad, 2001; Bunn and Arthington, 2002;
Arthington et al., 2006). Ideally, finding acceptable
values for these hydrologic metrics would become the
focus for management, rather than a one-size-fits-all
approach (Arthington et al., 2006) or a simple (and
generally infeasible) requirement to completely
restore the predisturbance flow regime.
Our objective was to identify hydrologic metrics for
small streams in the Puget Lowlands that would be
useful as flow management tools with the greatest
potential ecological relevance (i.e., hydrologic indicators).
FIGURE 2. Typical Annual Runoff Pattern (October to
September) Under Fully Forested and Highly Urbanized
Conditions Derived From Calibrated Kelsey Creek
Hydrologic Simulation Program-FORTRAN (HSPF) Model.
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We considered potentially useful stream hydrologic
indicators to: (1) be sensitive to land cover change
(i.e., forest clearing and subsequent urbanization)
consistent with expected hydrologic response, (2)
show statistically significant trends over time in
response to urbanization, (3) be correlated with measures of biological response to urbanization, and (4)
be relatively insensitive (uncorrelated) to potentially
confounding variables (e.g., basin area).
We also wished to compare the relative usefulness
of various urbanization measures used in previous
studies, specifically %TIA, in comparison with the
hydrologic metrics that best correlated with B-IBI.
We hypothesized that the selected hydrologic indicators would explain much more of the variance in
B-IBI scores if these metrics were indeed more direct
measures of the influence of urbanization on stream
biotic integrity. Our approach was retrospective and
relied on available land cover, basin area, continuous
flow, and benthic invertebrate data.
Once we had identified a set of hydrologic metrics
that we believed had the greatest potential as biologically relevant flow indicators, we performed an initial
evaluation of their application by comparing hydrologic indicators derived from continuous hydrologic
simulations of selected basin management alternatives for Miller Creek, one of the most highly urbanized basins used in our study. Through an interlocal
agreement, a multi-jurisdictional effort was initiated
to develop a basin plan that provided recommendations to improve the condition of Miller Creek (Executive Committee, 2006). The goal of the basin plan was
to identify measures that would protect the creek
from the impacts of existing and future development,
specifically aquatic habitat degradation, water quality, and flooding.
Because the Miller Creek basin was so highly
urbanized, the basin planning goal was set at restoring flow duration and magnitude to levels reflecting
75% forest, 15% grass, and 10% impervious cover
throughout the basin (75 ⁄ 15 ⁄ 10), including preexisting development. The preferred basin management
alternative included a requirement that all new
development in the basin comply with flow control
requirements that achieved runoff rates that matched
75 ⁄ 15 ⁄ 10 conditions and construction of enhanced
storage and outlet control at an existing regional
detention facility.
STUDY AREA
The Puget Lowland occupies a glaciated trough
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meable outwash deposits in valleys and along stream
corridors and less permeable till-capped plateaus. To
the west and east, uplands rise to the Olympic and
Cascade Mountains which are dominated by bedrock
and an overall surficial geology distinct from the lowlands (Booth et al., 2003).
The lowland region experiences a warm and wet
maritime climate, although winters are wetter than
summers – approximately three-fourths of the annual
precipitation falls in October through March. Precipitation ranges from about 1,000 mm per year near
sea-level and increases with elevation toward the
mountain crests as does the seasonal amount of
snowfall. Rainfall (and occasionally rain-on-snow) is
the dominant source of streamflow in the Puget Lowland, with highest flows in November through March
and lowest flows in July through September (Beechie
et al., 2006). During the winter wet season, rainfall is
of light to moderate intensity over an extended period
– typically one to several days. Puget Lowland
24-hour precipitation with a two-year return frequency is approximately 50 mm with intensity
increasing with elevation.
Historically, the Puget Lowland was primarily
covered by humid, temperate coniferous forests,
which were mostly cleared for timber in the 19th
and 20th centuries. Suitable lowlands and plateaus
were converted to farmland. Denser settlements
concentrated in lowlands near lakes, rivers, and
streams. Approximately four million people currently live in the Puget Sound basin and over a
million more inhabitants are expected in the next
decade, primarily in urban and suburban areas of
the Puget Lowland. Currently, second growth forests (often mixed with deciduous trees) are rapidly
being converted to residential and commercial uses
with associated losses of canopy interception and
soil moisture storage and increases in impervious
cover.
Historically, conveyance systems in developing
areas were designed to route rainfall runoff more
rapidly to streams, although progressively more protective regulations have resulted in increasing
amounts of mitigation – primarily on-site and regional flow detention facilities aimed initially at the
control of peak flows and currently aimed at matching predevelopment flow-duration curves (Booth and
Jackson, 1997; Booth et al., 2002). The most recent
regulations in King County, which only apply
to unincorporated rural areas, emphasize retention
of forest cover, minimization of impervious surface
cover, and minimization of soil compaction ⁄
removal (http://www.kingcounty.gov/property/permits/
codes/CAO.aspx). Nonetheless, most development and
retrofit attempts in the region predate the most recent
regulations.
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Although the stream monitoring site in the Issaquah
Creek basin is within the Puget Lowland boundaries,
the upper drainage of the sub-basin includes higher
elevation areas that result from the farthest westward intrusion of the Cascade mountain range into
the Puget Lowland (Booth et al., 2003).
The primary limitation on the number of paired
biology-hydrology sites used in our study was the
availability of continuous gauge records. A total of 16
locations were identified in 11 major creek basins in
King County that had at least one complete water
year (October to September) and calendar year of continuous flow data coincident with the benthic macroinvertebrate sampling year (Figure 1, Table 1). It
should be noted that these streams have not historically received any direct discharges of treated wastewater from regional treatment facilities and are
assumed to be affected primarily by the conversion of
upland forests to residential and commercial uses.
METHODS
Site Selection
We used a ‘‘space-for-time’’ approach that assumes
that spatial variation in the degree of urbanization of
subcatchments captures the historical temporal trend
in urbanization in the study area (Keenan and Ayers,
2002; Morse et al., 2003; Roy et al., 2003; Booth et al.,
2004; Fitzpatrick et al., 2004 – but see Wang et al.,
2000). Major assumptions of this approach include
that the spatial character of development reflects the
process of development over time and that other confounding factors such as underlying geology, climate,
or topography are controlled as much as possible by
confining the study area to a relatively homogeneous
physical and biological environment.
As the hypothesis is that the hydrologic regime is
of overarching importance in controlling the character of biological communities in Puget Lowland
streams, we broadened our selection of sites beyond
those that have typically been considered in previous
studies (Morley and Karr, 2002; Booth et al., 2004;
Cassin et al., 2005; Alberti et al., 2007) to include a
relatively undeveloped higher elevation sub-basin in
the Issaquah Creek basin near Hobart, Washington,
with significant amounts of bedrock that might mimic
the effects of impervious cover on streamflow (i.e.,
increase the frequency and magnitude of winter
storm flow and generate peak flows during summer).
Benthic Index of Biological Integrity
We selected the 10-metric Pacific Northwest B-IBI
recommended by Karr (1998) as our measure of
stream biological condition due to its historical use
in assessments of urbanization impacts on Puget
Lowland streams. The Pacific Northwest B-IBI represents four broad community characteristics that
include taxa richness and composition (five metrics –
total taxa richness, Ephemeroptera taxa richness,
TABLE 1. Continuous Flow Monitoring Time Period and Benthic Index of Biological Integrity (B-IBI) Sampling Year.
Stream Gauging
Map ID
Location
1
2
3
4
5
6
Bear
Evans
Des Moines
Issaquah near Hobart
North Fork Issaquah
Juanita
7
8
9
10
11
12
13
14
15
16
Kelsey
Laughing Jacobs
May
Miller
Rock
Covington
Jenkins
Little Soos
Soosette
Thornton
Gauge ID
B-IBI
Available Data
02e1
18a1
11d1
121206002
46a1
27a1
121205002
121200002
15c1
37a1
42a1
31l1
09a1
26a1
54i1
54h1
121280002
1995-2006
1988-2007
1992-2007
1987-2007
1989-2007
1993-2006
1964-1989
1956-2007
1992-2007
1990-2007
1992-2007
1996-2006
1989-2007
1989-2007
1996-2007
1995-2007
1997-2007
Sample ID
Year
BB9753
BBEVN11
DM_19954
ISISS41
ISNF11
JU_19954
1997
1999
1995
1995
1996
1995
KE_19954
LJ98us3
MA9713
MI9713
RO9823
SOOS041
JE9713
SOOS081
SOOS06a1
TH98DS3
1995
1998
1997
1997
1998
1995
1997
1997
1995
1998
Notes: Map ID refers to basins identified in Figure 1.
1
King County.
2
USGS.
3
Morley (2000).
4
J. Karr, data obtained from SalmonWeb http://www.cbr.washington.edu/salmonweb/, accessed January 18, 2006..
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Plecoptera taxa richness, Trichoptera taxa richness,
number of long-lived taxa), tolerant and intolerant
taxa (two metrics – number of intolerant taxa, percent tolerant individuals, excluding chironomids),
functional groups (two metrics – number of clinger
taxa, percent predator individuals), and percent dominance of the three most abundant taxa (one metric)
(Fore et al., 1996; Morley and Karr, 2002).
At each site, three replicate samples were collected
within a single riffle using a Surber sampler (Morley
and Karr, 2002). Samples were collected in late summer when rainfall is less frequent and intense, antecedent soil moisture is lowest, and flows are expected
to be relatively stable. Taxa richness is also high at
this time of year and sites are easy to access (Fore
et al., 1996). Taxonomic and classification results for
the three replicates were averaged and then assigned
a value of 1, 3, or 5 for each metric value. The ten
metric values were then summed resulting in a total
B-IBI score that ranges from 10 (considered very poor
biological condition) to 50 (considered excellent biological condition).
Due to the retrospective nature of our study, B-IBI
data (Table 1) were not always collected in the immediate vicinity of the stream gauging location. The farthest B-IBI sampling location occurred in the Kelsey
Creek basin and was almost 2 km upstream of the
gauging site with almost 55% of the basin drainage
occurring between the B-IBI site and the gauge. However, the level of development above the B-IBI sampling location and between the B-IBI station and the
gauge was very similar – in terms of %TIA the difference was less than 1%. The remaining B-IBI sampling locations were typically much closer, within
1 km of their respective gauging locations and ranged
from less than 1-14% of the total drainage area of the
basin.
Basin Characteristics
A variety of geographic information system datasets were available to characterize the level of urbanization, surficial geology, and other physical
characteristics of the catchment area upstream of the
stream gauging sites. Because previous assessments
of urbanization impacts on Puget Lowland streams
(Morley and Karr, 2002; Booth et al., 2004; Alberti
et al., 2007; Matzen and Berge, 2008) used a 1998
Landsat image classified into seven land cover categories (three urban, bare earth, forested, grass ⁄ shrub,
and open water) (Hill et al., 2003; Alberti et al., 2007)
and the selected benthic invertebrate sampling years
ranged from 1995 to 1999, we used the same land
cover dataset to calculate percent TIA, urban, and
non-urban forest land cover. Percent urban (%Urban)
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land cover was calculated as the proportion of land
cover in each sub-basin occupied by the three urban
categories (forested urban, grass ⁄ shrub urban, and
paved urban) represented in the classified image.
Percent Forest (%Forest) was calculated as the proportion of forested land in non-urban areas. Percent
TIA (%TIA) was based on estimates in Hill et al.
(2003) of impervious area within each of the seven
land cover classes.
We also used available electronic maps of surficial
geology (Booth et al., 2002), elevation (high resolution
LiDAR) (King Co. and Puget Sound LiDAR Consortium and USGS National Elevation Database), impervious surface cover based on remote multispectral
imaging conducted in 2000 (King County, 2004), and
mean annual precipitation (Daly et al., 2002) to further characterize the study basins. Basin characteristics derived from these sources included percent
cover of till, outwash, and bedrock exclusive of impervious surface cover (by using the 2000 impervious
surface cover data as a mask), mean basin slope
and elevation, local channel slope, and mean annual
precipitation.
Hydrologic Metrics
We obtained daily streamflow data from the King
County Hydrologic Information Center (http://green.
kingcounty.gov/wlr/waterres/hydrology/) and USGS
National Water Information System stream gauging databases (http://www.waterdata.usgs.gov/nwis)
(Table 1).
We selected a limited number of hydrologic metrics
for use in our analysis based on an initial evaluation
of a much larger suite of metrics by Cassin et al.
(2005). We reduced the list further by stipulating
that the selected metrics could be calculated with a
single year of daily mean flow data – water year or
calendar year depending on the metric. The final list
consisted of 15 metrics that included representatives
from the major flow regime categories of magnitude,
duration, timing, frequency, rate of change, and
flashiness ⁄ variability. A list of the hydrologic metrics
evaluated and a description of how they are calculated and their expected response to urbanization is
provided in Table 2.
Eleven of our metrics were derived from metrics
used in the Indicators of Hydrologic Alteration (IHA)
(Richter et al., 1996, 1997, 1998). We developed two
additional metrics from the IHA high and low pulse
metrics – low pulse range and high pulse range,
which measure the span of time in days between
the occurrence of the first and last pulse in each calendar year (low pulses) and water year (high
pulses).
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TABLE 2. Description of the 15 Hydrologic Metrics Used in This Study.
Component
Metric Name
Definition
Expected Response to
Urbanization
Units
Reference
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Frequency
Low pulse count
Increase
Count
Duration
Low Pulse Duration
Decrease
Days
Duration
Low Pulse Range
Number of times each calendar year that discrete low flow
pulses occurred
Annual average duration of low flow pulses during a
calendar year
Range in days between the start of the first low flow pulse
and the end of the last low flow pulse during a calendar year
Decrease
Days
High flow pulses occur more
frequently and although flow
magnitudes are higher, high
pulse durations are shorter
Increase
Count
Decrease
Days
Increase
Days
Increase
m3 ⁄ s per day
Increase
m3 ⁄ s per day
Increase
Count
Increase
Count
Richter et al.
(1996, 1997)
Increase
Count
Richter et al. (1998)
Decrease
Fraction
of year
Konrad and Booth (2002),
Konrad (2000)
Increase
Unitless
Baker et al. (2004)
Decrease
m3 ⁄ s
Earlier
Julian date
Richter et al.
(1996, 1997, 1998)
Clausen and Biggs (2000);
Richter et al. (1996, 1997,
1998)
High Flow Pulse
Occurrence of daily average flows that are equal to or
greater than a threshold set at twice (two times) the long-term
daily average flow rate
Frequency
High Pulse Count
Duration
High Pulse Duration
Duration
High Pulse Range
Number of days each water year that discrete high flow
pulses occur
Annual average duration of high flow pulses during a water
year
Range in days between the start of the first high flow pulse
and the end of the last high flow pulse during a water year
Richter et al.
(1996, 1997, 1998)
Richter et al.
(1996, 1997, 1998)
This study
Richter et al.
(1996, 1997, 1998)
Richter et al.
(1996, 1997, 1998)
This study
Various
Rate of
Change
Rate of
Change
Frequency
Fall Rate
Frequency
Rise Count
Frequency
Flow Reversals
Flashiness
TQmean
Flashiness
R-B Index
Magnitude
Seven-day minimum
Timing
Date of annual minimum
Rise Rate
Fall Count
The average rate of fall of all falling portions of the daily
hydrograph during a calendar year
The average rate of rise of all rising portions of the daily
hydrograph during a calendar year
The number of days of declining daily average flows during
a calendar year. A decline in flow is counted only when the rate
of change is greater than )10%
The number of days of increasing daily average flows during
a calendar year. An increase in flow is counted only when the
rate of change is greater than 10%
The number of times that the flow rate changed from an
increase to a decrease or vise versa during a water year. Flow
changes of less than 2% are not considered
The fraction of time during a water year that the daily
average flow rate is greater than the annual average flow rate
of that year
Richards-Baker Flashiness Index – A dimensionless index of
flow oscillations relative to total flow based on daily average
discharge measured during a water year
Centered seven-day moving average annual (calendar year)
minimum flow
Julian day of the date of the minimum daily average flow
during a calendar year
Note: All metrics based on daily average flow.
Richter et al.
(1996, 1997, 1998)
Richter et al.
(1996, 1997, 1998)
Richter et al.
(1996, 1997)
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Two metrics (TQmean and Richards-Baker Flashiness Index or R-B Index) came from previous studies
that focused on evaluating regional patterns and
trends in flow flashiness related to changes in land
cover ⁄ land use (Konrad and Booth, 2002; Baker
et al., 2004). TQmean has been used to detect trends in
flow flashiness related to basin urbanization in the
Puget Lowland (Konrad and Booth, 2002).
The IHA low and high pulse metrics typically
require an estimate of predisturbance (prior to forest
clearing and urbanization in the context of this
study) mean flow to determine the exceedance thresholds for identifying high and low flow pulses. Because
none of our gauge records predate the period of initial
forest clearing or urbanization and urbanization has
proceeded continuously in these basins since gauging
began (Konrad and Booth, 2002), we relied on available gauging data to estimate the mean flow and
pulse thresholds. We use 2 and 0.5 times the gauged
mean flow as thresholds for high (above threshold)
and low (below threshold) pulses, respectively. We
believe using gauged mean flow as the basis of the
pulse thresholds is reasonable given the uncertainty
in predisturbance mean flow and evidence that the
mean flow is not significantly altered by urban development in Puget Lowland streams (Konrad and
Booth, 2002).
An inherent assumption in our approach is that
the biological responses to changes in hydrology occur
over multi-year time scales (Konrad et al., 2005). Due
to the lack of sufficient hydrologic data to thoroughly
evaluate the appropriate temporal averaging period
for hydrologic metrics in relation to B-IBI scores, we
chose to average our hydrologic metrics over threeyears (preceding, but including the calendar year in
which the B-IBI sample was collected) when possible
based on the consideration that the long-lived taxa
(typically Plecoptera or stonefly genera) may live
more than three years in a stream (Stewart and
Stark, 1988). However, some gauging data at some
sites were only available for the year the B-IBI
sample was collected. Study basins, referenced to
Figure 1, data source, and years of available flow
data are provided in Table 1.
Data Analysis
We evaluated the relationship among the various
hydrologic metrics, land cover, and B-IBI scores of
the study streams by constructing bivariate correlation tables (Pearson’s r). We chose to use a parametric approach, rather than a nonparametric approach,
because our ultimate goal is the development of
predictive models, which rank correlation can not
provide. Data that did not meet the assumptions for
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parametric analysis were normalized using either
log10 (Low and High Pulse Count, Low and High
Pulse Duration, and Fall and Rise Rate) or arcsine
square root (%TIA, %Urban, and %Forest) transformations. We only discuss variables that were statistically significant based on the Benjamini and
Hochberg False Discovery Rate control procedure
(Verhoeven et al., 2005) to control the probability of
identifying spurious correlations (Type I error), while
minimizing the number of Type II errors.
Sensitivity of hydrologic metrics with a significant
correlation with B-IBI to potentially confounding
variables was assessed by evaluating the correlations
between each hydrologic metric and various basin
characteristics – Basin Area, %Outwash, %Till, Basin
Elevation, Local Channel Slope, and Precipitation.
Data that did not meet the assumptions for parametric analysis were normalized using either log10 (Local
Channel Slope) or arcsine square root (%Outwash
and %Till) transformations. Transformations for
%Bedrock and Basin Slope did not result in a normal
distribution and were not evaluated. Interactions
between measures of urbanization and B-IBI and
basin characteristics were also evaluated in the correlation matrix.
Principal components analysis (PCA) was conducted on the correlation matrix of the hydrologic
metrics with a significant correlation with B-IBI to
evaluate the major modes of variation and potential
redundancy (Clausen and Biggs, 2000; Olden and
Poff, 2003). We did not include the complete list of
hydrologic metrics in the PCA because we wanted to
focus only on those metrics that showed a strong
association with a measure of biological response to
urbanization. We used simple linear regression to
illustrate the strength of relationships between the
dependent variable B-IBI or land cover metrics
(%TIA, %Urban, and %Forest) and independent
hydrologic metrics, including the first principal component (PC) of the hydrologic metric PCA (Clausen
and Biggs, 1997).
We also evaluated the utility of the hydrologic
metrics that showed the strongest correlations with
B-IBI scores to detect the hydrologic impacts of urbanization. We performed nonparametric Mann-Kendall
trend tests on long-term datasets from two of our
study basins that have undergone rapid urbanization
over the periods of their hydrologic records (Juanita
and Kelsey) and one basin that is still relatively
undeveloped (Issaquah). We assumed that significant
trends in developing basins provide evidence for a
cause-and-effect relationship between urbanization
and hydrologic alteration and should corroborate the
space-for-time correlations with measures of urbanization. Establishing connections between urbanization and these metrics and evaluating trend detection
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capabilities provides support for the usefulness of
these metrics from a management perspective for
long-term trend detection monitoring.
Miller Creek Basin Plan Case Study
Continuous hydrologic model results were produced using a 50-year hourly rainfall record as part
of the planning process to simulate hydrology under
four conditions: (1) fully forested, (2) basin-wide
75 ⁄ 15 ⁄ 10 development (the planning goal), (3) current
conditions (based on 1995 land cover), and (4) the
preferred basin management alternative described
above – application of 75 ⁄ 15 ⁄ 10 flow control requirements for all new development and enhanced detention capacity. To evaluate the utility of our selected
hydrologic indicators in basin planning, using the
Miller Creek Basin Plan as a test case, we summarized and compared the long-term average (50 years)
of selected metrics under the four modeled conditions.
Traditionally, these basin flow management models
(developed using Hydrologic Simulation ProgramFORTRAN; HSPF) have been calibrated to predict
the timing, magnitude, and duration of winter peak
flow events (and annual and seasonal flow volumes)
as a result of historical focus on the control of flooding and channel erosion. The ability of these models
to predict various hydrologic metrics has not been
systematically tested. Therefore, we used a nonparametric Mann-Whitney U-test to evaluate the null
hypothesis that the difference in central-tendency of
the model-predicted and observed annual hydrologic
metrics is zero. A probability level of <0.05 was used
to test the null hypothesis and conclude that the
Miller Creek Basin Plan model could reproduce the
central-tendency of a particular flow metric. Comparison of modeled hydrologic indicators among the four
modeled conditions was limited to those metrics that
could be reliably predicted by the model.
RESULTS
Stream biological condition as measured by the
B-IBI ranged from 12 (very poor) to 44 (good) out of a
possible range of 10 to 50 (Figure 3). No sites were
classified as being in excellent condition (B-IBI ‡ 46).
Only one site (Rock Creek; B-IBI = 44) was in good
condition (B-IBI ‡ 36) and five sites were classified as
in very poor condition (B-IBI £ 16).
The basin areas represented by the selected gauging locations ranged from 10 to 54 km2 (Figure 3).
The measures of urbanization (%TIA and %Urban)
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did not include any minimally disturbed basins (i.e.,
forest dominated basins) but did include a range of
urbanization from relatively undeveloped rural to
some of the most highly urbanized basins with intact
streams (Miller and Des Moines Creeks) (Figure 3).
The degree of urbanization represented by %TIA and
%Urban, ranged from 10% to 59% and 15% to 89%,
respectively. Issaquah Creek near Hobart and Rock
Creek were the least urbanized and had the highest
(80%) non-urban forest cover. The surficial geology
of the study basins is dominated by till and outwash
deposits, although seven sub-basins that drain the
westernmost extension of the Cascades also contained
from 4% to 31% bedrock (Figure 3). With regard to
%Outwash, Rock Creek stood out among the other
basins with 56% of the basin in outwash deposits
(Figure 3). Table 3 lists the mean and range of all
measured landscape variables across the sites.
Basin mean annual flow ranged from 0.153 to
1.267 m3 ⁄ s, primarily reflecting the variation in basin
drainage area (Table 3). The selected hydrologic metrics presented a fairly wide range of values that we
hypothesize are primarily the result of the range of
levels of urbanization in our study basins (Table 3).
Data for the individual basins, including B-IBI
scores, basin characteristics, and mean values for the
15 hydrologic metrics are provided in Table S1.
Relationships Between Benthic Index of Biological
Integrity, Land Cover, and Hydrologic Metrics
We found statistically significant negative correlations between B-IBI and %TIA (r = )0.733; p < 0.01)
and B-IBI and %Urban (r = )0.748; p < 0.01) and a
significant positive correlation between B-IBI and
%Forest (non-urban) (r = 0.731; p < 0.01) (Table 4).
Each land cover metric explained roughly half of the
variance in B-IBI scores.
Six of the 15 hydrologic metrics (Low Pulse Duration, High Pulse Count, High Pulse Duration, High
Pulse Range, Flow Reversals, and R-B Index) were
significantly correlated (based on the Benjamini and
Hochberg False Discovery Rate control procedure)
with %TIA and %Urban (Table 4). The strongest correlations were between High Pulse Duration and
%TIA (r = 0.772; p < 0.001) and %Urban (r = 0.807;
p < 0.001). Weaker, but significant, correlations were
found with Low Pulse Duration and %TIA
(r = )0.587; p < 0.05) and %Urban (r = )0.589;
p < 0.05). Similar correlations with opposite signs
were found between all but one (Low Pulse Duration)
of the same six hydrologic metrics and %Forest
(Table 4). The signs of the statistically significant
correlations were consistent with their expected
response to urbanization (Table 2).
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FIGURE 3. Bar Charts Illustrating Distribution of Sub-Basin Characteristics for the 16 Sub-Basins Used in This Study.
Eight of the 15 hydrologic metrics (Low Pulse
Count, Low Pulse Duration, High Pulse Count, High
Pulse Duration, High Pulse Range, Flow Reversals,
TQmean, and R-B Index) were significantly correlated
with B-IBI (Table 4). The strongest correlation was
with High Pulse Range (r = )0.854; p < 0.0001) and
the weakest statistically significant correlation was
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with Flow Reversals (r = )0.652; p < 0.01). The sign
of the significant correlations was consistent with the
expected biological response to these metrics – B-IBI
scores increased in response to fewer Low Pulse and
High Pulse Counts and Flow Reversals, shorter High
Pulse Range, longer High Pulse and Low Pulse Duration, higher TQmean, and lower R-B Index. Only six of
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TABLE 3. Summary Statistics for Basin Characteristics and Hydrologic Metrics Calculated for 16 Stream Basins.
Variable
Basin characteristics
Basin area
%TIA
%Outwash
%Till
%Bedrock
%Urban
%Forest (non-urban)
Basin elevation
Precipitation
Basin slope
Local channel slope
Hydrologic metrics
Qmean
Low Pulse Count
Low Pulse Duration
Low Pulse Range
High Pulse Count
High Pulse Duration
High Pulse Range
Fall Rate
Rise Rate
Fall Count
Rise Count
Flow Reversals
TQmean
R-B Index
Seven-day minimum
Date of annual daily
minimum
Description (units)
Minimum
Mean
Maximum
9.6
9.5
9.7
18.4
0
15.1
4.8
75
891
6.7
0.004
28.8
33.5
24.6
35.3
5.4
52.8
38.2
146
1192
10.0
0.018
53.5
58.5
56
67.7
31.2
88.7
80.8
344
1833
21.2
0.090
0.153
2
7
67
0.490
10
26
207
1.267
28
93
341
2
2
34
10
7
168
22
31
306
Drainage basin area (km2)
1998 total impervious area (%)
Surficial outwash deposits (%)
Surficial till deposits (%)
Surficial bedrock cover (%)
1998 urban land cover (%)
1998 non-urban forest cover (%)
Mean basin elevation (m)
Mean annual precipitation (mm)
Mean basin slope (%)
Local channel slope (m ⁄ m)
Mean annual average flow (m3 ⁄ s)
Number of low pulse events per year (count)
Mean duration of low pulse events (days)
Range each calendar year over which low
pulse events occur (days)
Number of high pulse events per year (count)
Mean duration of high pulse events (days)
Range each Water Year over which
high pulses occur (days)
Average fall rate of falling flows (m3 ⁄ s per day)
Average rise rate of rising flows (m3 ⁄ s per day)
Number of falling flows (count)
Number of rising flows (count)
Number of flow reversals per year (count)
Fraction of year that daily flow exceeds mean
annual flow (fraction of year)
Richards-Baker Flashiness Index (unitless)
Minimum seven-day moving average flow (m3 ⁄ s)
Date of annual daily minimum flow (Julian date)
0.035
0.059
25
42
37
0.25
0.098
0.190
92
68
55
0.31
0.244
0.518
137
86
70
0.38
0.08
0.005
146
0.27
0.092
239
0.49
0.283
311
Note: Hydrologic statistics based on up to three years of data collected in years prior to and including the year benthic invertebrate samples
were collected.
these hydrologic metrics were also significantly correlated with the urbanization metrics. Low Pulse Count
and TQmean were significantly correlated with B-IBI
but not with the urbanization metrics.
or B-IBI scores. Unfortunately, no transformation
resulted in a normal distribution of %Bedrock or
Basin Slope, so evaluating the response of the
hydrologic metrics to these basin variables was not
possible.
Potential Confounding Variables
Principal Components Analysis
Several basin characteristics (%Outwash, Basin
Elevation, and Precipitation) were significantly correlated (p < 0.05) with the eight potential hydrologic
indicators, but these basin characteristics were also
significantly correlated with measures of urbanization and B-IBI scores. Local Channel Slope was significantly correlated with High Pulse Duration
(r = 0.513; p < 0.05) and Basin Area was significantly correlated with Flow Reversals, TQmean, and
R-B Index (r = )0.523; p < 0.05; r = 0.685; p < 0.05;
r = )0.503; p < 0.05, respectively) but not with
urbanization measures or B-IBI scores. Percent Till
was not significantly correlated with the eight
hydrologic metrics or with measures of urbanization
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The PCA showed that the first four PCs explained
94% of the total variance in the selected flow metrics
(Table 5). The first and second PCs (PC 1 and PC 2)
explained 76.4 and 9.1%, respectively, of the total
variance. Only the first PC had an eigenvalue greater
than 1.0, indicating that PC1 explains more variance
than any single predictor would (Kaiser, 1960). All
eight metrics load strongly (|r| > 0.8) on PC 1,
although High Pulse Count and High Pulse Range
also load less strongly on PC 2 (0.423 and 0.418,
respectively).
From Figure 4 and Table 5, we infer that PC 1
reflects an urbanization gradient associated with a
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TABLE 4. Pearson Correlation of Hydrologic Metrics With Measures of Urbanization,
Non-Urban Forest Cover, and Benthic Index of Biological Integrity Scores in 16 Stream Basins.
arcsin sqrt (%Total
Impervious Area)
Benthic Index of
Biological Integrity
log (Low Pulse Count)
log (Low Pulse Duration)
Low Pulse Range
log (High Pulse Count)
log (High Pulse Duration)
High Pulse Range
log (Fall Rate)
log (Rise Rate)
Fall Count
Rise Count
Flow Reversals
TQmean
R-B Index
Seven-day minimum flow
Date of annual daily
minimum
arcsin sqrt
(%Urban)
arcsin sqrt
(%Forest)
Benthic Index of
Biological Integrity
r
p
r
p
r
p
)0.733
0.001
)0.748
0.001
0.731
0.001
0.530
)0.587
0.087
0.700
)0.638
0.772
0.023
0.001
0.426
0.543
0.688
)0.527
0.736
)0.266
)0.364
0.035
0.017
0.750
0.003
0.008
<0.001
0.932
0.997
0.100
0.030
0.003
0.036
0.001
0.319
0.165
0.443
)0.589
)0.085
0.757
)0.638
0.807
0.048
0.020
0.372
0.506
0.681
)0.455
0.645
)0.188
)0.296
0.086
0.016
0.754
0.001
0.008
<0.001
0.859
0.941
0.156
0.046
0.004
0.077
0.007
0.485
0.266
)0.450
0.559
0.009
)0.716
0.634
)0.759
)0.003
0.024
)0.402
)0.530
)0.696
0.491
)0.683
0.245
0.381
0.080
0.025
0.973
0.002
0.008
0.001
0.990
0.931
0.123
0.035
0.003
0.053
0.004
0.360
0.145
r
p
)0.664
0.766
)0.036
)0.844
0.801
)0.854
)0.317
)0.265
)0.420
)0.529
)0.652
0.685
)0.703
0.057
0.475
0.005
0.001
0.896
<0.0001
0.0002
<0.0001
0.231
0.321
0.106
0.035
0.006
0.003
0.002
0.835
0.063
Note: Values given in boldface indicate significance based on Benjamini and Hochberg False Discovery Rate control (p = 0.05; k = 15)
(Verhoeven et al., 2005).
TABLE 5. Principal Component Analysis Loadings
(PC 1 through 4) of the Eight Hydrologic Metrics That
Were Significantly Correlated With Benthic Index
of Biological Integrity Scores in 16 Study Basins.
Variable
log (Low Pulse Count)
log (Low Pulse Duration)
log (High Pulse Count)
log (High Pulse Duration)
High Pulse Range
Flow Reversals
TQmean
R-B Index
Eigenvalue
Explained variance (%)
Cumulative explained
variance (%)
PC 1
PC 2
PC 3
PC 4
0.883
)0.930
0.866
)0.836
0.874
0.860
)0.827
0.915
6.116
76.4
76.5
)0.341
)0.172
0.423
)0.010
0.418
)0.032
0.393
)0.269
0.728
9.1
85.5
0.070
)0.066
)0.196
)0.397
)0.139
0.234
0.328
)0.103
0.398
5.0
90.5
0.005
)0.121
)0.115
0.359
0.030
0.389
0.067
)0.025
0.314
3.9
94.4
biological response that is significantly correlated
with all of the metrics – increasing urbanization
results in more High and Low Pulse Counts, shorter
High and Low Pulse Duration, longer High Pulse
Range, higher R-B Index, more Flow Reversals, and
lower TQmean. In general, there is a great deal of
redundancy with respect to the response to urbanization among these eight hydrologic metrics. PC 2
reflects a gradient orthogonal to PC 1 that is most
strongly related to changes in High Pulse Count and
Range.
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FIGURE 4. Plot of First Two Principal Components From Principal
Component Analysis of the Eight Hydrologic Metrics That Were
Significantly Correlated With Benthic Index of Biological Integrity
Scores. Numbers next to symbols are the basin map numbers
provided in Table 1.
Trends in Hydrologic Metrics
The eight hydrologic metrics that were significantly correlated with B-IBI also demonstrated statistically significant historical trends in at least one of
the two urbanized basins (Kelsey and Juanita – see
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TABLE 6. Summary of Mann-Kendall Test for Trend in Selected Hydrologic
Metrics in Two Rapidly Urbanizing and One Relatively Undeveloped Basin.
Kelsey1
Low Pulse Count
Low Pulse Duration
High Pulse Count
High Pulse Duration
High Pulse Range
Flow Reversals
TQmean
R-B Index
Juanita2
Issaquah3
tau
p
tau
p
tau
p
0.479
)0.460
0.265
)0.344
0.460
0.266
)0.488
0.731
<0.0001
<0.0001
0.006
0.0003
<0.0001
0.005
<0.0001
<0.0001
0.400
)0.348
0.332
)0.219
0.397
0.234
)0.326
0.665
0.004
0.01
0.02
0.12
0.005
0.10
0.02
<0.0001
)0.083
0.156
)0.090
)0.202
)0.032
)0.170
)0.093
0.208
0.43
0.14
0.39
0.055
0.77
0.10
0.38
0.046
Notes: Metrics calculated from long-term gauging records in Kelsey, Juanita, and Issaquah creeks. Values given in boldface indicate
significance (p < 0.05).
1
Kelsey Creek USGS 12120000 (1956-2007).
2
Juanita Creek USGS 12120500 (1964-1989).
3
Issaquah Creek USGS 12121600 (1964-2007).
Figure 1) and only one significant trend in the relatively rural basin (Issaquah) selected for long-term
trend analysis (Table 6). All eight metrics demonstrated trends consistent with the expected hydrologic response to urbanization in Kelsey Creek
(Figure 5), a highly urbanized basin in the county
with the longest period of flow observations. Six of
the eight hydrologic metrics (Low Pulse Count, Low
Pulse Duration, High Pulse Range, High Pulse
Count, TQmean, and R-B Index) demonstrated statistically significant trends in Juanita Creek (Table 6).
Comparison of Predictive Capability of Selected
Metrics
Although Table 4 provides a comparison of the relative strength of the correlation between urbanization and hydrologic metrics with B-IBI scores, it
would also be instructive to look at the slopes and
prediction confidence intervals among the metrics
that best correlate with B-IBI and include a comparison with the first hydrologic PC1 (Clausen and Biggs,
1997). In Figure 6 we show the least-squares fit
regression line and the 95% prediction confidence
intervals for %TIA, Low Pulse Count, Low Pulse
Duration, High Pulse Count, High Pulse Duration,
High Pulse Range, Flow Reversals, TQmean, R-B
Index, and PC1. The regressions with the highest
slope and smallest prediction confidence intervals are
High Pulse Count, High Pulse Range, and PC1.
Initial Application of Selected Indicators – Miller
Creek Basin Plan
Based on the Mann-Whitney U-test, we determined
that the Miller Creek HSPF model could reliably
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predict six of the eight hydrologic metrics that were
also significantly correlated with B-IBI scores – the
two Low Pulse metrics, the three High Pulse metrics,
and TQmean. The model did not reliably predict Flow
Reversals or the R-B Index based on the Mann-Whitney test.
Comparison of the six reliably predicted hydrologic
indicators calculated from the basin planning model
results for fully forested, current, plan goal, and the
preferred planning alternative indicated that the plan
goal of 75 ⁄ 15 ⁄ 10 flow matching could be achieved
with the preferred alternative (Executive Committee,
2006). However, the difference between the plan goal
and fully forested conditions for TQmean and High
Pulse Duration was relatively small (10-25% absolute
difference), while the difference between the plan
goal and fully forested conditions for the Low Pulse
Count and Duration and High Pulse Count and
Range metrics was still substantial – 70-270% difference (Table 7).
DISCUSSION
Our results suggest that the hydrology of urbanizing basins in the Puget Lowlands has a significant
influence on the biotic integrity of streams. Eight of
the fifteen metrics we evaluated were significantly
correlated with B-IBI scores (Low Pulse Count, Low
Pulse Duration, High Pulse Count, High Pulse Duration, High Pulse Range, Flow Reversals, TQmean, and
R-B Index) and all but two (Low Pulse Count and
TQmean) were significantly correlated with measures
of urbanization.
High Pulse Count and High Pulse Range are measures of frequency and the period of time each year
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FIGURE 5. Time Series Plots Showing Kelsey Creek Trends in the Eight Hydrologic Metrics That Were Significantly Correlated
With Benthic Index of Biological Integrity Scores. Statistically significant trends in all eight hydrologic metrics were identified in Kelsey
Creek, a basin which has one of the longest complete daily hydrologic records (1956-2003) that cover a period of rapid urbanization.
that high pulse events occur. We re-evaluated trends
in High Pulse Count separately for wet (October to
March) and dry (April to September) periods in the
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two urbanizing and one rural basin and found significant trends in wet period pulse counts in one urbanizing basin (Juanita, tau = 0.410; p = 0.006) and in
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FIGURE 6. Regression of Selected Hydrologic Metrics and Hydrologic PC1 vs. Benthic Index of Biological Integrity
Scores. Plots include 95% prediction confidence intervals. Symbols are the basin map numbers provided in Table 1.
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526
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PUGET LOWLAND, WASHINGTON, USA
TABLE 7. Comparison of Low and High Pulse Metrics and TQmean Under Modeled Fully Forested Condition, Current (1995)
Conditions, and Two Management Scenarios Based on Continuous Hydrologic Modeling Conducted for the Miller Creek Basin Plan.
Metric
Units
Low Pulse Count
Low Pulse Duration
Low Pulse Range
High Pulse Count
High Pulse Duration
High Pulse Range
TQmean
# per year
Days
Days
# per year
Days
Days
Fraction of year
Fully
Forested
Current
Conditions
3
44
136
7
4
99
0.34
13
11
193
25
3
317
0.26
(333)
()50)
(42)
(72)
()25)
(220)
()24)
Plan Goal
11
13
204
17
3
266
0.31
(267)
()70)
(50)
(143)
()25)
(169)
()9)
Preferred
Alternative
10
13
193
16
3
256
0.31
(233)
()70)
(42)
(129)
()25)
(168)
()9)
Notes: Results presented are averages of model results for the period 1950 to 2002 and the percent difference with fully forested conditions is
shown in parentheses. The Plan Goal (75 ⁄ 15 ⁄ 10) reflects a practical management goal in this basin, which assumes a target of 75% forest,
15% grass, and 10% impervious cover (Level 2 or 75 ⁄ 15 ⁄ 10) throughout the basin for all existing development and existing regional control
facilities in place. The Preferred Alternative includes Level 2 (75 ⁄ 15 ⁄ 10) flow management requirements for new development and
additional storage at regional detention facilities.
both urbanizing basins for dry period pulse counts
(Juanita, tau = 0.301; p = 0.035; Kelsey, tau = 0.477;
p < 0.00001) and no significant trends in wet or dry
periods (p > 0.05) in the largely rural and forested
Issaquah basin. It appears that High Pulse Count
increases as a result of increasing numbers of high
pulses during winter and the occurrence of high
pulses in summer as a basin becomes increasingly
urbanized more summer high pulses occur than
would have occurred historically.
Although the PCA indicated a great deal of redundancy among the eight potential hydrologic indicators, the ability of High Pulse Count and High Pulse
Range to satisfy the four management characteristics,
combined with the results of the comparison of the
predictive capability of each hydrologic metric, suggests that High Pulse Count and High Pulse Range
are the individual hydrologic metrics with the greatest potential for biological relevance. We should note
that even though these metrics displayed the smallest B-IBI prediction confidence intervals (along with
PC1), these prediction intervals span over half the
range in B-IBI scores (see Figure 6) suggesting that
at best these metrics could discriminate between the
worst and best B-IBI locations.
We applied a fairly conservative test for statistical
significance (Benjamini and Hochberg False Discovery Rate control) to minimize Type I and II errors in
the identification of metrics related to urbanization
and biotic integrity. The p-values for the correlations
between %TIA and Low Pulse Count and TQmean were
0.035 and 0.036, respectively. A significant correlation between TQmean and %TIA in Puget Lowland
streams has been noted previously (Booth et al.,
2004). It is likely that our statistical approach was
overly conservative in this case and that the correlation between %TIA and TQmean is not spurious.
We were unable to eliminate the possibility that
some basin characteristics (%Outwash, Basin ElevaJOURNAL
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tion, and Precipitation) potentially confound the relationships identified (or potentially explain some of
the residual variation) between hydrologic metrics
and urbanization and hydrologic metrics and B-IBI
scores. These three potential confounding variables
were also found to be correlated with measures of
urbanization and B-IBI scores (%Outwash, Basin Elevation, and Precipitation). The correlation between
elevation and B-IBI scores has been noted previously
and attributed to the concentration of forest clearing
and development in lowlands and less disturbance at
higher elevations, resulting in a statistical, but unlikely causal, relationship between elevation and B-IBI
(Fore et al., 1996; Morley and Karr, 2002). We suggest that the relationship with %Outwash and Precipitation is caused by the same covariation of
development with elevation and concentration of
development along streams, rivers, and lakes where
outwash deposits are typically found.
Local Channel Slope may be a confounding factor
for High Pulse Duration, and Basin Area appears to
be a confounding factor for Flow Reversals, TQmean,
and R-B Index. However, %Till does not appear to be
a confounding factor as it was not significantly correlated with the eight hydrologic metrics or with measures of urbanization or B-IBI scores. The potentially
confounding relationships between Basin Area and
TQmean (Konrad and Booth, 2002) and R-B Index
(Baker et al., 2004) have been noted previously, and
consideration should be given to controlling for these
effects if these metrics are used for management or
further research into flow-ecology relationships.
Unfortunately, we could not test the potential confounding effect of %Bedrock because of its non-normal distribution in our dataset. Only seven of the 16
basins contained measurable amounts of bedrock
ranging from 4% to 31%. The highest sub-basin
(Issaquah near Hobart) with 20% bedrock had the
lowest %TIA, but a ‘‘fair’’ B-IBI score of 32, more
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DEGASPERI, BERGE, WHITING, BURKEY, CASSIN,
consistent with the response to the potential hydrologic indicators than to %TIA (Figure 6), providing
some circumstantial evidence that bedrock and ⁄ or
elevation ⁄ precipitation driven hydrologic differences
were a significant factor controlling the biological
integrity of this stream. The lower elevation subbasin with the highest amount of bedrock (May
Creek) was moderately urbanized (%TIA = 24.5) and
had a ‘‘poor’’ B-IBI score of 24 that appeared fairly
consistent with %TIA and the potential hydrologic
indicators (Figure 6), suggesting that the hydrologic
differences in Issaquah near Hobart are due more to
elevation ⁄ precipitation than to bedrock.
The PCA results indicate that there is a great deal
of redundancy among these eight metrics with
respect to their response to urbanization. We suggest
that the eight metrics found to be correlated with BIBI scores are all surrogate measures of the increase
in the frequency of occurrence of high flow pulses in
winter and summer and associated low flow pulses
during summer. These high and low flow summer
pulses did not typically occur under historically forested conditions. Benthic invertebrates that are best
able to withstand these flow disturbances (i.e., small,
mobile, short-lived species that have multiple reproductive cycles throughout the year – multivoltine species), would become more abundant than larger, less
mobile species (with only one or two annual reproductive cycles – univoltine or semivoltine species).
Consistent with this hypothesized disturbance mechanism, mayflies of the genus Baetis (many of which
are small and multivoltine) occur with greater
relative abundance in our more urbanized streams
(Cassin et al., 2005). Dominance of samples by a few
mayfly (Ephemeroptera) taxa that are not clinger or
predator taxa; a lack of stoneflies, caddis flies, and
generally intolerant long-lived species; and a high
percentage of tolerant taxa – typically taxa in the
Plenariidae (flatworms), Hirudinea (leeches) or Simuliidae (black flies) – results in lower B-IBI scores.
Six of the eight hydrologic metrics correlated with
B-IBI also consistently identified trends over time in
two urbanizing basins (Juanita and Kelsey) with
long-term data records – Low Pulse Count, Low Pulse
Duration, High Pulse Range, High Pulse Count,
TQmean, and R-B Index. Consistent with the observations of Baker et al. (2004), R-B Index appeared to
the most sensitive trend detection metric (highest tau
values) due to its low inter-annual variability and
clear response to the hydrologic effects of urbanization. Baker et al. (2004) found that the inter-annual
variability of the R-B Index was much lower than
most IHA metrics and had much greater power to
detect trends in flow flashiness based on 100 randomly selected stream gauges from six Midwestern
states. Detected trends in TQmean in Juanita and
JAWRA
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Kelsey Creeks (and lack of trend in Issaquah Creek)
confirm the trends in TQmean previously identified by
Konrad and Booth (2002) in the same creeks.
Although R-B Index appears to be the most sensitive
metric for detecting trends related to urbanization
based on our limited evaluation, it is one of the weakest ‘‘predictors’’ of B-IBI scores – on par with the
urbanization measures %TIA and %Urban. We
should also emphasize that flow monitoring for the
purpose of trend detection will likely only identify
impacts long after they have occurred or will be confounded by the large inter-annual climate variability
and the time window available for trend analysis
(Konrad and Booth, 2002).
We noted a consistent significant inverse relationship between several of our hydrologic metrics and
urbanization measures and non-urban forest cover.
The inverse relationship between urbanization measures and non-urban forest cover has been recognized
previously with respect to phosphorus concentrations
in King County streams (Brett et al., 2005; Alberti
et al., 2007). The strong inverse relationship between
urbanization and forest cover has been attributed to
the predominant mode of land transformation from
forest to suburban and urban development in King
County (Brett et al., 2005). The correlation between
%TIA and %Urban with %Forest in our basins was
)0.998 (p < 0.0001) and )0.987 (p < 0.0001), respectively.
TQmean is the only hydrologic metric we have
selected that has already been shown to have a statistically significant linear relationship with B-IBI
scores from the Puget Lowland (Morley and Karr,
2002; Booth et al., 2004). The most recent research
has consistently pointed to the importance of the connectedness of impervious surfaces to streams as
urban areas expand (Hatt et al., 2004; Walsh, 2004).
This has resulted in a shift from the most general
measures (or surrogates) of urbanization such as
impervious surface cover (%TIA; Booth et al., 2004;
Alberti et al., 2007; Matzen and Berge, 2008) or percent of urbanized area (%Urban; Morley and Karr,
2002) to road crossings ⁄ road density (Konrad et al.,
2005; Alberti et al., 2007), hydrologic metrics (Booth
et al., 2004), and number of stormwater connections
to the stream (Walsh, 2004). Here we have provided
further evidence for the suggestion of Booth et al.
(2004) that hydrologic metrics may be a more direct
measure of the effects of urbanization on stream biota
– specifically benthic macroinvertebrates.
Although our study does not provide empirical evidence for mechanistic links (i.e., cause and effect)
between summer flow pulses and B-IBI scores, our
findings support the hypothesis that species composition of macroinvertebrates in Puget Lowland streams
now favors species tolerant of summer flow pulses
528
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that did not occur historically. We believe our study
results provide a starting point for further research
that could test our hypothesis and further refine our
understanding of the mechanisms involved.
Case Study Application
Modeled Miller Creek Basin planning alternatives
for TQmean and High Pulse Range, illustrate the ability of the preferred alternative to meet the plan goal
and the disparity between the plan goal and fully
forested conditions for these metrics (Figure 7). The
difference between the preferred management alternative and fully forested conditions for TQmean was
relatively small, while the difference between the preferred management alternative and fully forested
conditions for the Low and High Pulse metrics was
still rather large, suggesting that the biological
benefit (if the pulse metrics are truly a predictor of
biological condition and the effect of development
on stream biota can be reversed) of the preferred
management alternative may be less than what is
suggested based on consideration of TQmean alone.
Additional model calibration would be required to
more reliably predict Flow Reversals and R-B Index
so they could also be compared. We suspect that
pulses are easier to predict as it is necessary only to
capture the exceedance of the pulse thresholds rather
than match a particular flow magnitude or duration.
We do not mean to imply that restoration in such
a highly urbanized basin should strive for complete
hydrologic restoration (see Booth et al., 2004), but we
want to draw attention to the fact that regulating
runoff from new development and improvements in
URBANIZING STREAMS
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PUGET LOWLAND, WASHINGTON, USA
performance of a large regional detention facility can
influence TQmean, but has much less influence on the
Low and High Pulse metrics.
Historically, mitigation of the impacts of development on stream hydrology has focused narrowly on
structural and site-specific end-of-pipe remedies
(Booth et al., 2002). Although this approach has
reduced peak flows and mitigated flooding and erosion problems (Booth et al., 2002), there is little
evidence that this approach has effectively protected
biological resources downstream of these measures
(Maxted and Shaver, 1997; Horner et al., 1999, 2002;
Maxted, 1999). Furthermore, mitigation requirements
have applied only to new development and exceptions
are granted for developments below a certain threshold, which on a parcel scale may result in insignificant impacts, but on a cumulative basin-scale may be
significant (Booth and Jackson, 1997). If development
is socially desired, focus should be on maintaining
natural flow patterns (including summer months) at
the basin-scale and avoidance of direct discharge to
streams (Walsh et al., 2005).
Implications for Modeling
Given the difficulty of separating urbanization
effects from other factors (climate, basin area, soils,
geology, elevation, precipitation, etc.) that weaken
the ability to unequivocally identify hydrologic indicators for management purposes (and the lack of predisturbance B-IBI and flow data), a hydrologic
modeling approach in addition to the gauge-data
approach used in our study might prove to be very
useful (Richter et al., 1998; Poff et al., 2006; Sanborn
FIGURE 7. Comparison of TQmean and High Pulse Range Under Modeled Fully Forested Condition, Current (1995) Conditions, and Two
Management Scenarios – Plan Goal and the Preferred Alternative. The Plan Goal reflects a practical management goal in this basin, which
assumes a target of 75% forest, 15% grass, and 10% impervious surface cover (75 ⁄ 15 ⁄ 10) throughout the basin with existing regional control
facilities in place. The Preferred Alternative includes 75 ⁄ 15 ⁄ 10 flow management requirements for new development and additional storage
at regional detention facilities.
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529
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DEGASPERI, BERGE, WHITING, BURKEY, CASSIN,
and Bledsoe, 2006). For example, it might be helpful
to use models to extrapolate flow metrics to B-IBI
sampling locations that are not located near existing
flow gauges (e.g., Cassin et al., 2005). There are far
more historical B-IBI sampling locations in the Puget
Lowland than we have used in this study. For example, Alberti et al. (2007) evaluated relationships
between B-IBI and a variety of urbanization patterns
using a dataset consisting of 36 separate B-IBI locations in 14 distinct Puget Lowland stream systems,
but noted the general lack of gauge records for their
sampling sites. A broader selection of sites would
allow further testing and identification of flow-ecology
relationships. This approach would also provide datasets suitable for construction of statistical models
that might control for confounding variables or
include additional explanatory variables as no one
cause or variable will likely explain all of the variation in B-IBI scores (Alberti et al., 2007).
In King County, long-term hydrologic modeling of
current, future, and fully forested conditions (as
described above) has been the foundation of basin
planning for the past 20 years. This modeling
approach provides the data (albeit synthetic) needed
to calculate our annual hydrologic metrics and compare the results obtained from models of fully forested and current conditions and any modeled
management scenario (see the Miller Creek Basin
Plan example above). The range of variability
approach described by Richter et al. (1998) using
modeled predevelopment (fully forested) and postdevelopment (current or management scenario) conditions to quantify the degree of hydrologic alteration
(Shiau and Wu, 2004) within a particular basin might
be another way to use models to control for the
effects of variation in climate and basin-specific characteristics as part of a basin-scale regional water
resources assessment program.
Implications for Other Regions
Although the hydrologic response to urbanization
in any particular region depends on a variety of factors that include development types and patterns, hydroclimate, geology, physiography, vegetation, and
catchment size (Poff et al., 2006), we would expect to
see a biological response in regions where urbanization affects normally stable summer base-flows. We
also suspect that our conceptual approach to linking
hydrologic alteration to biological impairment in
urbanizing streams could be adapted for use in other
regions with different flow regimes and biological
communities to help filter the large number of flow
metrics down to those that are most likely to be biologically relevant.
JAWRA
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CONCLUSIONS
Our initial criteria for selecting hydrologic indicators identified eight hydrologic metrics that are significantly correlated with a measure of stream biological
condition (Pacific Northwest B-IBI) and six of these
hydrologic metrics were significantly correlated with
measures of urbanization. However, only six of the
eight metrics consistently demonstrated trends in
urbanizing basins (Low Pulse Count, Low Pulse Duration, High Pulse Count, High Pulse Range, TQmean,
and R-B Index). Three of eight indices (Flow Reversals,
TQmean, and R-B Index) were compromised by their correlation with basin area and one was compromised by
correlation with Local Channel Slope (High Pulse
Duration). Only two of the hydrologic metrics tested,
High Pulse Count and High Pulse Range met all four
criteria – they were most highly correlated with B-IBI
and measures of urbanization (%TIA and %Urban),
reliably detected trends and were not confounded by
basin area. The increase in these high pulse metrics
with respect to urbanization is the result of an increase
in winter high pulses and the occurrence of high pulse
events during summer (increasing the frequency and
range of high pulses), when practically none would
have occurred prior to development.
If future research continues to support this hypothesis, it would have significant implications for stormwater management in Puget Lowland streams.
Stream biota might be better protected by minimizing
the amount of new land developed and minimizing
the number of direct connections and the amount of
runoff from impervious surfaces. Low Impact Development approaches might be an attractive approach
to minimizing summer runoff as the volume of water
delivered during this period in the Puget Lowland is
much smaller relative to winter runoff volumes (Konrad and Burges, 2001; Holman-Dodds et al., 2003;
Hood et al., 2007). Low Impact Development
approaches, especially those that include infiltration
of runoff, have the added benefit of potentially reducing the toxic effects of contaminated runoff. Social,
economic, and political measures to preserve existing
forest cover should not be overlooked as preserving
the historical landscape (i.e., forest and soils) provides the historical flow regime that in turn provides
the highest degree of certainty that native biological
communities will be adequately protected.
SUPPORTING INFORMATION
Additional Supporting Information may be found
in the online version of this article:
530
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LINKING HYDROLOGIC ALTERATION
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Table S1. Summary Statistics for the 16 Study
Basins, Including the 15 Hydrologic Metrics and
B-IBI Scores.
Please note: Neither AWRA nor Wiley-Blackwell is
responsible for the content or functionality of any
supporting materials supplied by the authors. Any
queries (other than missing material) should be directed to the corresponding author for the article.
ACKNOWLEDGMENTS
This work was supported with funding from King County’s
Normative Flow Project (http://www.kingcounty.gov/environment/
watersheds/general-information/normative-flow-studies.aspx). Miller
Creek Basin Plan HSPF modeling results were provided by Jeff
Jacobson, King County, Stormwater Services. We thank Kyle Comanor, Steve Foley, Josh Latterell, Gino Lucchetti, and Randy
Shuman for internal review of the draft manuscript, and Brian
Murray, David St. John, Kate O’Laughlin, and Randy Shuman for
programmatic support for the Normative Flow Project. We also
wish to thank Edwin P. Maurer, Santa Clara University, for his
suggestions and comments. Thoughtful and critical reviews were
provided by two anonymous reviewers that greatly improved the
final manuscript.
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