Urbanization

Chapter 11

Urbanization: Hydrology, Water Quality, and Influences on Ecosystem Health Fran Sheldon, Catherine Leigh, Wendy Neilan, Michael Newham, Carolyn Polson and Wade Hadwen Australian Rivers Institute, Griffith University, Nathan, QLD, Australia

Chapter Outline 11.1 Introduction 11.2 Methods 11.2.1 Study sites and study design 11.2.2 Hydrology 11.2.3 Water quality 11.2.4 Macroinvertebrate assemblages 11.3 Results and discussion 11.3.1 Hydrology 11.3.2 Water quality 11.3.2.1 Dissolved oxygen

229 231 231 232 235 235 236 236 236 236

11.3.2.2 Water temperature 238 11.3.2.3 Electrical conductivity 239 11.3.2.4 pH and impacts of iron precipitate at the Blunder Creek WSUD site 239 11.4 Macroinvertebrate assemblage composition 242 11.4.1 General patterns 242 11.4.2 Assemblage composition 242 11.4.3 Diversity of EPT taxa 243 11.5 Conclusions 245 References 247

ABSTRACT Urbanization significantly alters the hydrology of catchments, the transport of sediment, nutrients, and pollutants and consequently has a degrading impact on urban stream biota. In response to these impacts, water sensitive urban design (WSUD) is often seen as an approach to reduce the hydrological and pollutant risks for receiving waterbodies. We explored the impacts of urbanization across three stream types: an urban system, one subject to upstream WSUD development, and a forested stream. Our results suggested that there were strong differences in the hydrology, water quality, and macroinvertebrate assemblage composition between the FOREST site and the two sites impacted by urbanization; the WSUD and the URBAN sites. Despite the upstream WSUD development, both the WSUD and URBAN sites had hydrology characterized by higher rates of rise and fall of flood peaks. However, very little variation in macroinvertebrate assemblage composition across the three streams could be directly attributed to the changed hydrology. Rather it was differences in water quality that showed the strongest influence on the biota, suggesting that even when measures are implemented to help restore more natural flow patterns, degraded water quality can have an overriding influence on stream ecosystem health. Keywords: Hydrology; Macroinvertebrates; Urbanization; Water quality.

11.1 INTRODUCTION Urbanization has significantly altered the hydrology of catchments and consequently the transport of sediment, nutrients, and pollutants. These changes begin with the initial impacts of deforesting catchments into spaces for agriculture or urban development, which significantly increases runoff and the transport of sediment, nutrient, and organic matter into receiving waterways (Heaney and Huber, 1984; Paul and Meyer, 2001; Hawley and Vietz, 2016). Thereafter, conventionally developed urban catchments tend to be characterized by having very high proportions of impervious area (Fig. 11.1; Walsh et al., 2005). This affects the hydrology and ecology of urban streams in a number of ways. First, the impervious surfaces inhibit the infiltration of rainfall into soils and, therefore, can limit the recharge of groundwater resources (Brown et al., 2009).

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229

230 Approaches to Water Sensitive Urban Design

Little to no rainfall

Strom event

Low flow High flow

Very low flow Very high flow

DOC input Very high DOC input

Oxic conditions Very well oxygenated conditions

Low dissolved oxygen but not anoxic Low dissolved organic carbon (DOC)

High dissolved organic carbon

Medium dissolved organic carbon

Medium dissolved organic carbon

Forested catchment

WSUD catchment

Urbanised catchment

Macroinvertebrate assemblages (types and abundances) Few taxa senstive to low dissolved oxygen conditions

Some taxa sensitive to low dissolved oxygen conditions

Many taxa sensitive to low dissolved oxygen conditions

Some taxa tolerant of low dissolved oxygen conditions

Many taxa tolerant of low dissolved oxygen conditions

FIGURE 11.1 Conceptual model of the impacts of urbanization in stream ecosystems. Within the model, the two extremes of the hydrological continuum are represented (No Flow and Storm events) for each of the three upstream catchment conditions FOREST, WSUD and URBAN). WSUD developments vary in their extent and are mostly designed to reduce the hydrological impacts of urbanization but removing the flashiness of flows. From Sheldon, F., Pagendam, D., Newham, M., McIntosh, B., Hartcher, M., Hodgson, G., Leigh, C., Neilan, W., 2012b. Critical Thresholds of Ecological Function and Recovery Associated with Flow Events in Urban Streams. Urban Water Security Research Alliance Technical Report No. 99.

Second, impervious surfaces capture and direct rainfall into networks of drains and pipes, which can quickly discharge into urban creeks. The consequences of this networking are classically observed in the hydrographs of urban streams, where rainfall events are much flashier, with higher flow peaks and shorter durations than equivalent events in forested catchments (Walsh et al., 2005; Burns et al., 2012; see also Chapter 10).

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The consequences of urban hydrology on the ecology of urban streams have received considerable attention in the literature. Not only does the stormwater delivered through piped networks represent an incredibly powerful erosive force (Burns et al., 2012) but also carries a wide range of contaminants and pollutants that stress receiving waterbodies and their biota (Herlihy et al., 1998; Grimm et al., 2005). The responses of biota in urban streams have been studied at multiple scales, with researchers highlighting how changes in hydrology, sediment, and nutrient loads can have a transformational effect on the way that stream ecosystems function (Grimm et al., 2005; Kaye et al., 2006; Walsh et al., 2007; Tsoi et al., 2011; Burns et al., 2012). Given the evidence of the impacts of urbanization on stream ecosystems and the critical role of impervious surfaces in changing the routing and speed of water from rainfall events entering waterways (Fletcher et al., 2013), there has been a huge investment in urban stormwater management since the 1990s (Burns et al., 2012). Most of these efforts are commonly referred to as water sensitive urban design (WSUD), which is an approach to manage stormwater in urban developments to reduce the hydrological and pollutant risks for receiving waterbodies (Roy et al., 2008). WSUD designs typically are engineered systems that seek to retain stormwater and treat it before discharge into urban streams (Roy et al., 2008). The pollutant reduction targets for WSUD assets can vary, but typically the stated objectives focus on reducing total suspended sediments entering waterways by around 80% and reducing total nitrogen (N) and total phosphorus (P) loads by 45% (Parker, 2010). There are a wide range of WSUD features that can be implemented to address stormwater management in urban contexts. These include, but are not restricted to, infiltration trenches, porous paving, rain gardens, rainwater tanks, swales, sediment basins, gross pollutant traps, and constructed wetlands (Parker, 2010). Despite the proliferation of designs and their implementation over the past 20 years, there are surprisingly few field-based assessments of WSUD system performance (but see Parker, 2010 and Adyel et al., 2016). This lack of field assessments creates two issues. First, new designs and approaches are not being improved using real-world experience and evidence. This is a problem, particularly given the rapid evolution of WSUD, because all designs are predicated on laboratory or pilot-scale performance studies that have not always been adequately scaled up, or repeated in different contexts, to provide support for implementation (Parker, 2010). Second, it remains unclear as to whether the condition of receiving waterbodies has responded, in either a negative or a positive way, to WSUD implementation. This is less a question of engineering, design, and monitoring of WSUD assets and more a challenge around environmental monitoring and ecosystem health assessment. There are many technical challenges in this space, including the mismatch in scale of WSUD implementation and the scale of threats to the condition of urban streams (Peterson et al., 2011) and the fact that long lag times are common, as we seek to understand how ecosystems respond to management interventions (Tsoi et al., 2011). In this chapter, we seek to examine urban stream ecology in the context of urban development by contrasting the hydrology, water quality, and ecosystem health, as measured by macroinvertebrate assemblages, across three stream reaches with different upstream conditionsdforested, WSUD, and urban. We used indices based on macroinvertebrate family presence and their abundance at each site as our biological measure of ecosystem health. Macroinvertebrates as a broad group show a diverse range of tolerances to anthropogenic disturbance, and changes in the presence and abundance of specific groups, particularly the sensitive EPT taxa, provide good indications of disturbance (Clarke et al., 2003). The EPT taxa, insects of the orders Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies), are a particularly sensitive group often used in ecosystem health assessment (Lenat and Crawford, 1994; Sponseller et al., 2001). The aim here is to add to our knowledge base around how streams might respond to WSUD implementation, at a large scale, and to inform our future approaches to WSUD performance assessment. Fig. 11.1 presents a conceptual model of the impacts of urbanization in stream ecosystems. Within the model, two extremes are represented (FOREST and URBAN). WSUD developments vary in their extent and are mostly designed to reduce the hydrological impacts of urbanization by removing the flashiness of flows. The capacity for any WSUD development to restore, or maintain, ecosystem health should depend on the extent to which it was able to recreate the natural flow regime of the stream ecosystem. Hence, we would expect water quality and ecosystem health parameters to vary along this continuum, reflecting the degree to which WSUD is implemented in any given catchment.

11.2 METHODS 11.2.1 Study sites and study design Three broad reaches of three streams in South East Queensland, Australia, with different upstream catchment conditions (FOREST, WSUD, and URBAN) were chosen to explore temporal trends in hydrology, water quality, and macroinvertebrate assemblage composition over a 12-month period, from winter (dry) through to summer (wet) (June 2011eMay 2012),

232 Approaches to Water Sensitive Urban Design

TABLE 11.1 Description and Location of the Three Gaged Catchments

Stream and suburb

FOREST

WSUD

URBAN

Tingalpa Creek, Sheldon

Blunder Creek, Daintree Crescent, Forest Lake

Stable Swamp Creek, Sunnybank

Latitude (degree)


27.57


27.62


27.58

Longitude (degree)

153.18

152.97

153.05

Upstream catchment area (ha)

2785

360

442

Total impervious area upstream (%)

1

42

38

Catchment slope (%)

1

1.1

1.5

From Chowdhury, R., Gardner, T., Gardiner, R., Hartcher, M., Aryal, S., Ashbolt, S., Petrone, K., Tonks, M., Ferguson, B., Maheepala S., McIntosh, B.S., 2013. SEQ Catchment Modelling for Stormwater Harvesting Research: Instrumentation and Hydrological Model Calibration and Validation. Urban Water Security Research Alliance Technical Report No. 83.

reflecting the annual change from dry to wet conditions. As we were aiming to reduce site-based differences and maximize upstream catchment differences, sites were selected so they had similar site-based riparian cover (>60%). All sites were instrumented with hydrological monitoring stations that collected both hourly flow and water quality data. The three stream reaches were Tingalpa Creek (FOREST treatment), Blunder Creek tributary (WSUD treatment), and Stable Swamp Creek (URBAN treatment) (Table 11.1; Fig. 11.2). This design may be considered pseudoreplicated as each treatment was isolated to a specific stream. To overcome this problem, we chose three stream reaches, separated by approximately 50 m of stream, which were considered to be “independent” sites, and assessed our findings against the predictions from the conceptual model (Fig. 11.1). We were limited to three streams through the availability of continual data loggers. Tingalpa Creek (FOREST) is an upland creek in the Redlands region of South East Queensland; the upstream catchment was completely forested. At the sampling sites along the creek reach, the channel comprised bedrock riffles, runs and pools with complex microhabitats of snags (fallen timber), tree roots, and macrophytes (Fig. 11.2). Stable Swamp Creek (URBAN) and the tributary to Blunder Creek (WSUD) were both in the OxleyeBlunder Creek subcatchment of the lower Brisbane River. The upstream reaches of Stable Swamp Creek (URBAN) are highly urbanized, the channel is degraded and deeply incised, and pools have deep silt with poor habitat quality, mostly dominated by introduced macrophytes. Upstream of the sampling reach, there is a large off-stream wetland that most likely plays a role in baseline hydrology, particularly after large flow events. The WSUD site, a tributary of Blunder Creek, was downstream of Forest Lake, a large stormwater retention basin constructed during the 1990s as part of a masterplan residential development; the channel is deeply incised and degraded with mostly poor habitat quality. The presence of good riparian vegetation cover on both banks, and associated in-channel woody debris and relatively stable bank structure, has allowed some riffle development along the channel. Given the nature of the WSUD development at Forest Lake, we would expect the major impacts on the downstream tributary to Blunder Creek to be associated with changes in hydrology, with most of the impervious surfaces associated with urbanization upstream of the Forest Lake impoundment. Based on the conceptual model (Fig. 11.1), we would predict the hydrological patterns evident at the WSUD site to resemble those of the FOREST site more than those of the URBAN site. However, despite the expected positive impact on hydrology of Forest Lake on the WSUD site and the presence of good riparian vegetation in the reach, the immediate surrounding area was highly urbanized and likely to have a strong influence on both water quality and macroinvertebrate assemblage diversity (Walsh et al., 2007).

11.2.2 Hydrology Hydrological data for the three catchments was obtained from a larger dataset of 12 catchments, which had been instrumented (for full details, see Chowdhury et al., 2013). A tipping bucket rain gage (0.2 mm) and pressure transducer with data logger measured continuous 6-min rainfall and water height data, respectively, at each site from 2009 until the end of 2012. Gaps in the hydrology data were filled using linear interpolation, which fills the gaps in the data by drawing a straight line between the ends of the data where the gap occurs (Marsh, 2004). A series of hydrological metrics (see Table 11.2) were calculated using daily flow data and summarized by calendar month for the period January 2009 until December 2011. These metrics were chosen based on their relevance to the conceptual understanding of hydrological impacts on ecological health in urban streams (Walsh et al., 2005; Walsh and Kunapo, 2009). To explore patterns in the

Urbanization: Hydrology, Water Quality, and Influences on Ecosystem Health Chapter | 11

233

153°20’E

153°00’E 27°20’S

27°20’S

Bulimba creek Tingalpa creek

Bribane river

WSUD FOREST URBAN 27°40’S 27°40’S Logan river Oxley creek Pimpama river

153°00’E

153°20’E

URBAN

WSUD

FOREST FIGURE 11.2 Position of the three study sites, FOREST, WSUD and URBAN, on streams in South East Queensland and photos taken at each site.

234 Approaches to Water Sensitive Urban Design

TABLE 11.2 Description of the Variables Calculated From the Daily Flow Data (Marsh, 2004) Variable Name

Variable Acronym

Variable Description

Minimum

Min

Minimum is the smallest value for flow recorded for the time period.

Maximum

Max

Maximum is the largest value for flow recorded for the time period.

Percentile 10

P 10

The 10th percentile is the value that is exceeded by 10% of the records.

Percentile 90

P 90

The 90th percentile is the value that is exceeded by 90% of the records.

Mean daily flow

MDF

The mean daily flow is a measure of central tendency and is calculated as the average of the records (sum of values/number of days in the time period).

Median daily flow

Med

The median is the “middle” value for the entire record: it is the value exceeded 50% of the time. For flow data, the median is usually much lower than the mean daily flow because the distribution of discharge data is negatively skewed with a lower limit of zero and no upper limit.

Coefficient of variation

CV

The CV of daily flow is the mean of all daily flow values divided by the standard deviation for the daily flow values.

Standard deviation

STD

The standard deviation is a measure of how widely the values are dispersed from the mean value. The standard deviation has the same units as the input data.

Skewness

Skw

Skewness is a measure of how different the mean and median are. Skew ¼ mean/median.

Number of zero flow days

Zer

The number of zero days counted. Note that the number of zero flow days does not include days with a missing record unless they are filled with zero values.

Number of high spell (5)

HS(5)Num

Number of times the flow exceeded five times the mean flow value for the time period.

Number of high spell (10)

HS(10)Num

Number of times the flow exceeded 10 times the mean flow value for the time period.

Number of low spell (0.5)

LS(0.5)Num

Number of times the flow fell below half the mean flow value for the time period.

Number of low spell (0.1)

LS(0.1)Num

Number of times the flow fell below one 10th of the mean flow value for the time period.

Longest low spell (0.5)

LS(0.5)Long

Duration (in days) of longest low flow event below half the mean flow value for the time period.

Mean of low spell (0.5) troughs

LS(0.5)Peak

Mean of low spell troughs. Low spell threshold was set at half the mean flow value for the time period.

Mean duration of low spell (0.5)

LS(0.5)MeanDur

Mean duration (in days) of low spell events. Low spell threshold was set at half the mean flow value for the time period.

LS(0.5)TotDur

LS(0.5)TotDur

Total duration (in days) of low spell events for the time period. Low spell threshold was set at half the mean flow value for the time period.

Number of rises

NumRise

Number of continuous periods of rise.

Mean magnitude of rises

MMagRise

Mean difference in the flow values between the start and end of the rise.

Mean duration of rises

MDurRise

Mean duration of periods of rise.

Total duration of rises

TotDurRise

Total duration of periods of rise.

Mean rate of rise

MRateRise

Mean rate of rise.

Greatest rate of rise

GreatRatRise

The fastest rate of rise.

Number of falls

NumFall

Number of continuous periods of fall.

Mean magnitude of falls

MMagFall

Mean difference in the flow values between the start and end of the fall. Continued

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235

TABLE 11.2 Description of the Variables Calculated From the Daily Flow Data (Marsh, 2004)dcont’d Variable Name

Variable Acronym

Variable Description

Mean duration of falls

MDurFall

Mean duration of periods of fall.

Total duration of falls

TotDurFall

Total duration of periods of fall.

Mean rate of fall

MRateFall

Mean rate of fall.

Greatest rate of fall

GreatRateFall

The fastest rate of fall.

Baseflow index

BFI

Ratio of baseflow to total flow in a period.

Flood flow index

FFI

Ratio of nonbaseflow to total flow in a period.

Mean daily baseflow

MDBF

Mean of baseflow in a period.

hydrological data, we used a multivariate analysis technique, principal component analysis (PCA), to help visualize hydrological similarities, or differences, between the study streams (Quinn and Keough, 2002). PCA can summarize complex multidimensional data into fewer dimensions allowing the strong drivers of difference (in our case, specific calculated hydrological metrics) to be determined. Differences in hydrological metrics were explored between treatment streams.

11.2.3 Water quality A water quality sonde was installed in each catchment at the same site as the gage (for full details, see Chowdhury et al., 2013) to measure pH, dissolved oxygen (DO), turbidity, and electrical conductivity at hourly intervals. Water quality data for each metric were converted into daily maximum, minimum, mean, median, and range values. To understand the influence of water quality on macroinvertebrate assemblage composition, the median monthly data for the sample months were calculated from the daily data because macroinvertebrates are more likely to respond to the background water quality regime, rather than specific water quality measurements on any one sample day.

11.2.4 Macroinvertebrate assemblages Urbanization, through changes in hydrology, can adversely impact habitat diversity and complexity within streams (Walsh et al., 2005). Recognizing this, we mapped each site into distinct microhabitats, including “riffle,” “pool,” “macrophyte (aquatic plants),” and “snag (woody debris).” Replicate samples from each microhabitat were taken by sweeping a 250 mm mesh pond net over an area approximating 5 m2 for 20 s. Samples were returned to the laboratory where they were sorted and macroinvertebrates identified to family using a stereo microscope and the abundance of each distinct taxa recorded. For analyses, both the complete community data and the subset of EPT: Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies) abundance data were used. To explore differences in assemblage composition between the three sites, a suite of multivariate analysis techniques was used (see Quinn and Keough, 2002). To reduce the influence of extremely abundant or rare taxa on patterns, data (abundance of each macroinvertebrate family in each sample) were square root transformed before analysis. To explore changes in assemblage composition between sites and through time, the BrayeCurtis similarity coefficient (Quinn and Keough, 2002) was applied to the transformed data. This calculates a value of similarity between each sample based on the presence and abundance of each macroinvertebrate family. All taxa occurring in one or more samples were retained in the subsequent analyses. Using the BrayeCurtis similarity matrix, analysis of similarities (ANOSIM) was used to statistically explore differences between each stream, with these differences displayed visually using nonmetric multidimensional scaling (MDS) ordination. To explore which families might be contributing to the differences between sites, the similarity percentages procedure (SIMPER) was used (Clarke and Gorley, 2001). Finally, to explore the influence of background water quality and hydrology on assemblage composition, monthly median values for recorded water quality parameters and calculated hydrology metrics were generated. The BIOENV routine was then used to explore which variables from each of the two environmental datasets best explained the observed patterns in the macroinvertebrate assemblage dataset. All multivariate analyses were conducted in the Primer-E Software package (PRIMER-E: www.primer-e.com).

236 Approaches to Water Sensitive Urban Design

11.3 RESULTS AND DISCUSSION 11.3.1 Hydrology In keeping with the conceptual model on the influence of urbanization on stream hydrology (Walsh et al., 2005), both the URBAN and WSUD sites had higher total discharge and more frequent discharge events than the FOREST site (Fig. 11.3). When the hydrological metrics (Table 11.2) were calculated from the monthly data (2009e11) from the three sites and analyzed using a PCA analysis, there was clear overlap in the hydrological signature of all three sites. The distribution of the sample months along PC1 (x-axis) reflects the flashiness of the monthly hydrology as measured by mean daily flow and the magnitude of the rate of rise and fall of flood flows. Only some months from the URBAN and WSUD sites showed clear differences through their distribution along PC1 (x-axis) (Fig. 11.4), associated with variation in the number of high flow events, the magnitude and rate of the flood rising limb, and the magnitude of the flood falling limb (Fig. 11.4). These hydrological metrics describe how flashy any individual event is. The position of the three streams on the PCA plot suggests that the URBAN and WSUD sites had, overall, more variable flows between months, with some months having high flows that were characterized by rapid rates of rise and fall. The spread of monthly hydrology points along PC2 (y-axis) suggested similar variability between all three sites in monthly baseflow conditions, as measured by the baseflow index (BFI), and flood conditions, as measured by the flood flow index (see Table 11.2). To better understand the influence of upstream catchment on hydrology, the sequence of very high flow months (December 2010 and January 2011) in the URBAN and WSUD sites was removed. In the resulting reduced PCA, separation of monthly hydrology was more apparent (Fig. 11.5). The distribution of the sample months along PC1 again reflects the flashiness of the monthly hydrology, as measured by mean daily flow, and the magnitude of the rate of rise and fall of flood flows, whereas the distribution along PC2 reflects monthly minimum and baseflow conditions. The distribution of hydrological sample months from the FOREST stream is less variable across both PC1 and PC2, suggesting lower temporal variability in flood magnitude and rate of rise and fall, as well as less temporal variability in low flows and BFI. The URBAN site tended to have the greatest temporal variability, as indicated by the more dispersed hydrological sample months across both axes. Overall, using the suite of hydrological metrics in a PCA, the hydrological changes imposed by the upstream WSUD intervention on the Blunder Creek WSUD site appeared to contribute to it being much more similar to the FOREST site than to the URBAN site.

11.3.2 Water quality 11.3.2.1 Dissolved oxygen DO varied throughout the year with mean daily DO generally much lower in the summer months (December to February) compared with the winter months, across all sites (Fig. 11.6a). Interestingly, the URBAN site had much higher mean daily

ML/day per hectare of catchment

1.65

1.24

0.83

0.41

1.04E-003 February 10, 2009

October 31, 2009

July 22, 2010

April 11, 2011

December 31, 2011

FIGURE 11.3 Hydrograph of daily flow (ML/day per hectare of catchment) for FOREST (Green), WSUD (Blue), and URBAN (Red) sites between 2009 and 2011. From Sheldon, F., Pagendam, D., Newham, M., McIntosh, B., Hartcher, M., Hodgson, G., Leigh, C., Neilan, W., 2012b. Critical Thresholds of Ecological Function and Recovery Associated with Flow Events in Urban Streams. Urban Water Security Research Alliance Technical Report No. 99.

Blunder creek (WSUD)

PC2 (12% variation; low flow duration, base and flood index)

2.0

Stable swamp creek (URBAN) Tingalpa creek (FOREST)

1.0

.0

–1.0

–2.0

–3.0 –2.0

.0

2.0

4.0

PC1 (37% variation; magnitude and rate of flow rise and fall) FIGURE 11.4 Principal component analysis (varimax rotation) of hydrological metrics calculated from monthly flow data from 2009 until 2011 for three streams in South East Queensland; an URBAN site (Stable Swamp Creek), a WSUD site (upstream Blunder Creek), and a FOREST site (Tingalpa Creek) (see Table 11.2).

PC2 (14% variation; minimum and low flows, baseflow index)

3.0

Blunder creek (WSUD) Stable swamp creek (URBAN) Tingalpa creek (FOREST)

2.0

1.0

.0

–1.0

–2.0 –4.0

–2.0

.0

2.0

4.0

PC1 (35% variation; magnitude of flood rise and fall) FIGURE 11.5 Principal component analysis (varimax rotation) of hydrological metrics calculated from monthly flow data from 2009 until 2011 for three streams in South East Queensland with the extremely high flow months from Fig. 11.3 removed; an URBAN site (Stable Swamp Creek), a WSUD site (upstream Blunder Creek), and a FOREST site (Tingalpa Creek) (See Table 11.2).

238 Approaches to Water Sensitive Urban Design

(a)

(b) Blunder creek (WSUD)

Blunder creek (WSUD)

Stable swamp creek (URBAN) Tingalpa creek (FOREST)

12.0 24 h dissolved oxygen range (mg/l)

Mean daily dissolved oxygen (mg/l)

12.0

10.0

8.0

6.0

4.0

Stable swamp creek (URBAN) Tingalpa creek (FOREST)

10.0

8.0

6.0

4.0

2.0

2.0

.0

.0 January 1, 2009 January 1, 2010 January 1, 2011 January 1, 2012 July 1, 2009 July 1, 2010 July 1, 2011 July 1, 2012

Date

January 1, 2009 January 1, 2010 January 1, 2011 January 1, 2012 July 1, 2009 July 1, 2010 July 1, 2011 July 1, 2012

Date

FIGURE 11.6 (a) Mean daily dissolved oxygen (DO) (mg/L) levels across all three sites for the period of data logging (January 2009eJune 2012) and (b) daily DO range (mg/L) across all three sites for the period of data logging (January 2009eJune 2012). Note that continual logging of all sites commenced in January 2011.

DO levels across most of the year compared with either the WSUD or FOREST sites, possibly reflecting the higher baseflow at this site. The daily range in DO is often used as an indicator of stream health (Bunn et al., 2010), with lower daily ranges seen to be more typical of healthy streams. Across our sites, both the WSUD and URBAN sites had larger daily ranges across all seasons compared with the FOREST site (Fig. 11.6b). These DO results are consistent with how we understand water quality in urban creeks. Generally DO is much lower across the seasons because of increased respiration rates associated with higher organic loads from impervious surfaces in urban catchments (Blunder Creek) unless water flow is maintained (Stable Swamp Creek). Intuitively, upstream WSUD should reduce nutrient and organic inputs to urban streams and therefore reduce the likelihood of large DO fluctuations. However, our WSUD site is downstream of Forest Lake, a large stormwater retention basin, and while this system appears to positively influence some aspects of hydrology (as outlined previously), it is eutrophic and likely increases nutrient levels in the downstream tributaries. Interestingly, daily mean DO levels in the FOREST site were often extremely low, particularly in the summer (Fig. 11.6a) possibly reflecting the position of the sonde in a pool within the creek, the high organic load from riparian leaf litter, and the often low-flow conditions at the FOREST site between storm events. This suggests that at times during the year, conditions in the forested catchment will be just as severe as in the urbanized catchments. However, in the urbanized catchments, there are likely to be other drivers of poor health, including increased loads of labile organic carbon from road runoff, high heavy metals, and other pollutants that will exacerbate the ecosystem health impacts of low levels of DO.

11.3.2.2 Water temperature Water temperature at the three sites varied across the year as expected, with maximum daily temperatures around 10e15 C during the winter months (June, July, August) and summer temperatures around 25e30 C (Fig. 11.7a). Maximum daily temperature and daily temperature range (over 24 h) at a site are considered to be good indicators of stream health (Bunn et al., 2010), with higher maximums and temperature ranges indicative of poorer stream health. However, despite similar riparian conditions across the three sites, which should have mediated water temperature through shading, higher maximum daily temperatures were found at both WSUD and URBAN sites when compared with the FOREST site (Fig. 11.7a). Although the three sites in this study were chosen because of their good riparian cover, they differed in the extent of upstream riparian cover. Only the forested Tingalpa Creek (FOREST) has continuous riparian cover upstream of the sample site, and the influence of this extensive shading can be seen in its mean daily temperature

Urbanization: Hydrology, Water Quality, and Influences on Ecosystem Health Chapter | 11

(a)

(b) Blunder creek (WSUD)

6

Stable swamp creek (URBAN) Tingalpa creek (FOREST)

Daily temperature range (°C)

Maximum daily temperature (°C)

30

239

25

20

Blunder creek (WSUD)

Stable swamp creek (URBAN) Tingalpa creek (FOREST)

5

4

3

2

15 1

10

0

January 1, 2009 January 1, 2010 January 1, 2011 January 1, 2012 July 1, 2009 July 1, 2010 July 1, 2011 July 1, 2012

Date

January 1, 2009 January 1, 2010 January 1, 2011 January 1, 2012 July 1, 2009 July 1, 2010 July 1, 2011 July 1, 2012

Date

FIGURE 11.7 (a) Maximum daily temperature ( C) across all three sites for the period of data logging (January 2009eJune 2012) and (b) daily temperature range ( C) across all three sites for the period of data logging (January 2009eJune 2012). Note that continual logging of all sites commenced in January 2011.

range that is nearly 1.5 C lower than either the URBAN or WSUD sites. The much higher daily temperature range in the WSUD and URBAN sites most likely reflects upstream discontinuous riparian cover. This suggests that for good stream health outcomes, even when WSUD is implemented, the condition of the upstream catchment can have an overriding influence (Sheldon et al., 2012a).

11.3.2.3 Electrical conductivity High levels of water electrical conductivity can also pose risks for stream health. Increased conductivity has been associated with land use change, usually the result of converting forested catchments into agricultural or urban landscapes (Suárez et al., 2017). We would therefore expect higher conductivities in the more urbanized streams. However, across the three sites, the role of urbanization in increased stream conductivity was not clear. Highest conductivity values were observed in the WSUD site (Fig. 11.8) with little apparent difference between the URBAN and FOREST sites. The lower overall conductivity in the URBAN site may reflect the higher baseflow recorded in that system compared with the FOREST site, where low flows and isolation of instream pools were common during the drier months. The exact cause of the higher baseflow in the URBAN site is unclear; it may have been associated with deep drainage through garden watering or leaking local water mains. However, there was also an isolated off-stream wetland upstream from the URBAN site that may have contributed to baseflow through alluvial flow paths. The much higher conductivity observed in the tributary to Blunder Creek (WSUD) is likely the result of considerable dissolved iron entering the stream at that point. The exact cause of the iron floc in the stream was unclear, but probably reflected biogeochemical processes occurring within the soils surrounding the stream.

11.3.2.4 pH and impacts of iron precipitate at the Blunder Creek WSUD site pH was not expected to vary greatly between the three streams, as pH has not been observed to vary greatly in streams across South East Queensland (Bunn et al., 2010; Sheldon et al., 2012a) and stream acidification is not a major issue. However, when the minimum daily pH from the three sites was compared, the Blunder Creek WSUD site had a significantly lower minimum pH (analysis of variance: F2,1139 ¼ 60.286, P < .001) with average minimum values of <6.5 (Fig. 11.9). Interestingly, the FOREST site also had quite variable pH values during the observation period with a number of extremely low values (Fig. 11.9); the sonde at the Tingalpa site was positioned in a pool, and it is likely that under

240 Approaches to Water Sensitive Urban Design

Blunder creek (WSUD) Stable swamp creek (URBAN) Tingalpa creek (FOREST)

Mean daily electrical conductivity (µS/cm)

1200

1000

800

600

400

200

0 January 1, 2009 January 1, 2010 January 1, 2011 January 1, 2012 July 1, 2009 July 1, 2010 July 1, 2011 July 1, 2012

Date FIGURE 11.8 Mean daily conductivity (mS/cm) across all three sites for the period of data logging (January 2009eJune 2012). Note that continual logging of all sites commenced in January 2011.

7.5

Daily minimum pH

7.0

6.5

6.0

5.5

5.0 Blunder creek (WSUD)

Stable swamp creek (URBAN)

Tingalpa creek (FOREST)

Site FIGURE 11.9 Box plots (minimum, first quartile, median, third quartile, and maximum) of recorded daily minimum pH across all three sites for the period of data logging (January 2009eJune 2012). Note that continual logging of all sites commenced in January 2011.

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241

certain low-flow conditions, respiration increased within the pool may have caused these occasional drops in pH. The extremely low average minimum pH at the WSUD Blunder Creek site is likely to have been caused by an increase in iron floc and associated dissolved iron at the site. Iron precipitates are a natural and common occurrence even in pristine streams (Abesser et al., 2006; Duckworth et al., 2009). They occur where dissolved ferrous iron from groundwater enters streams and comes in contact with an environment where oxygen is abundant (Fig. 11.10; Schwertmann, 1991; Rhoton et al., 2002). Ferrous iron oxidation in Natural stream in low flow conditions 1

Water infiltrates soil, and oxygen is consumed by respiration.

2

Water moves into the oxic/anoxic zone. Depleted oxygen means altemative electron acceptors are used in metabolism. Iron is reduced as electron acceptor and mobilized, yet altemating oxidation states retard its movement.

3

Water moves through anoxic zone where some reduced iron is mobilized.

4

Reduced iron is oxidized in-stream and precipitatesas iron oxy/hydroxides. A small amount of iron precipitate is visible during low flows.

1

Unsatuarted zone Periodic saturation

Oxic zone Alternates oxic/anoxic

2

4 3

Permanent saturation

Anoxic zone

Incised stream in low flow conditions 1

Water infiltrates soil, and oxygen is consumed by respiration.

2

Water enters the oxic/anoxic zone. Low oxygen levels mean that altemative electron acceptors are used. This area may be much larger in depth as incised stream channel casues drop in watertable.

3

More water travels through and enters stream from anoxic zone, which was previously generally below the stram channel. More watre movement through this zone may mobilize larger amount of reduced iron.

4

Larger amounts of iron precipitate visible in incised stream. Water entering from anoxic zones carries more reduced iron, which oxidizes on contact will oxygen. 1 Unsaturated zone

Periodic saturation

Oxic zone

2

Altemates oxic/anoxic 3

Permanent saturation

4

Anoxic zone

FIGURE 11.10 Conceptual model of iron precipitation in a natural stream and exacerbated conditions in an incised urban stream. From Sheldon, F., Pagendam, D., Newham, M., McIntosh, B., Hartcher, M., Hodgson, G., Leigh, C., Neilan, W., 2012b. Critical Thresholds of Ecological Function and Recovery Associated with Flow Events in Urban Streams. Urban Water Security Research Alliance Technical Report No. 99.

242 Approaches to Water Sensitive Urban Design

streams can have negative impacts at sites where it occurs. The oxidation and hydrolysis reactions of ferrous iron produce Hþ ions that can cause both acidification and salinization of stream waters (Singer and Stumm, 1970): Fe2þ þ 1=4O2 þ Hþ ¼ Fe3þ þ 1=2H2 O Fe3þ þ 3H2 O ¼ FeðOHÞ3 ðsÞ þ 3Hþ The release/oxidation of ferrous iron into streams in excess can contribute to water quality and habitat degradation, and it has been known for more than 20 years that high levels of iron in streams reduce macroinvertebrate abundance and density (Rasmussen and Lindegaard, 1988). Circumstances that may enhance the release of ferrous iron into streams include forest clearing and reduced evapotranspiration, which increases deep drainage, runoff, and dissolved organic matter loads, leading to a greater load of available iron (Albert et al., 2005). Industrial and urban effluents and runoff may also contribute to excess iron loads in streams, causing degradation and in extreme cases smothering the benthic habitat (Nedeau et al., 2003). From observations made during fieldwork at the Blunder Creek WSUD site, we suggest that symptoms of urbanization on streams may also lead to increased loads of iron and precipitates. In particular, we noticed that incision and downcutting of streams, caused by increased runoff from impervious surfaces, expose previously buried subsoils, rich in ferrous iron minerals. Because subsurface flow is released from these deeper soil layers into the stream channel, it may carry with it a greater load of iron than water discharged from shallower soil layers. Fig. 11.10 presents a conceptual model of iron precipitation in a natural stream. The model describes the changes that occur as water moves through the soil, changing its percent oxygen saturation and thereby mobilizing iron that occurs within the soil due to bacterial anaerobic respiration.

11.4 MACROINVERTEBRATE ASSEMBLAGE COMPOSITION 11.4.1 General patterns Almost 75,000 individuals were collected from 106 taxa across the 12-month sampling period. The mean species richness per sample (number of species per sample) differed between sites (analysis of variance F2,101 ¼ 5.679, P < .01); the FOREST site had a mean richness per sample of 18 (0.8), the WSUD site had a mean richness of 21 (1.5), while the URBAN site had a mean richness of 22 (1.0) (Table 11.3; Fig. 11.11). Similarities in species richness do not represent similarity in species composition; rather an increase in species richness is often associated with disturbance because factors that cause disturbance in streams, such as higher nutrient loads, often create conditions for large numbers of invasive “pest” species. To better understand the influence of urbanization on stream assemblage composition, the composition of the overall assemblage was considered, along with the presence and abundance of sensitive taxa such as the EPT taxa (Sponseller et al., 2001).

11.4.2 Assemblage composition Macroinvertebrate assemblage composition differed significantly between the three sites (ANOSIM Global R ¼ 0.678, P < .001). MDS allows a two-dimensional visualization of the similarity in assemblage composition for any two samples. Samples (depicted by points) that are closer together in two-dimensional space are more similar in terms of assemblage composition. For an explanation of MDS, see Quinn and Keough (2002). Differences can be seen in the visual representation of the similarity between samples from each site (Fig. 11.12). The assemblage of macroinvertebrates from the FOREST site was distinctive compared with that from either the URBAN or WSUD site, with only minor overlap in similarity between samples from the WSUD and URBAN sites (Fig. 11.12). Samples from the URBAN site, regardless of habitat, had a within-site similarity of 43.5% and were dominated by Dipterans from the family Chironominae, or midges, with the Chironominae contributing 33%, while the Oligochaeta (worms) contributed a further 12%. Microcrustaceans contributed a further 6.5% and the hydrobiid gastropods (snails) a further 6%. The within-site similarity of all samples from the WSUD site was 44.5% with the major contribution to withinsite similarity from the microcrustaceans (14%) and gastropods (snails) of the families Planorbidae and Physidae (24%) (Fig. 11.13). In contrast, the within-site similarity of all samples from the FOREST site was 40%, with the Leptophlebiid mayflies contributing 30%, the chironomids (midges) a further 22%, and the Aytid shrimps a further 8.5% (Fig. 11.13). Of the long-term logged water quality variables, median daily DO (mg/L), mean daily pH, and mean daily temperature range ( C) explained 33% of the variation in assemblage composition. The long-term hydrology metrics, however, explained little variation in the assemblage composition, with only 15% explained by a combination of the rate of

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243

TABLE 11.3 Average Number of Individuals Per Sample Collected From the Three Sites Over the 12-Month Sampling Period. Sensitive EPT Taxa are Highlighted in Gray Scientific Name Phyla

Class

Common Name

Cnidaria

Hydrozoa

Hydras

Blunder Ck (WSUD)

Stable Swamp Ck (URBAN)

Tingalpa Ck (FOREST)

5.4

1.0

1.2

Platyhelminthes

Flatworms

32.8

4.2

0.6

Nemertea

Proboscis worms

1.2

0.2

0.0

Annelida

Mollusca

Oligochaeta

Worms

99.4

148.9

8.1

Hirudinea

Leeches

0.4

1.3

0.0

Gastropoda

Snails

577.5

75.5

1.5

Nematoda Arthropoda

56.3

2.0

0.1

Arachnida

Acari

Mites

Roundworms

101.0

3.3

1.3

Crustacea

Microcrustaceans

Water fleas

377.1

60.5

3.8

Decapoda

Shrimps

1.1

0.6

26.3

Isopoda

Slaters

0.0

0.0

0.3

Coleoptera

Beetles

0.5

5.6

13.6

Hemiptera

Bugs

1.3

24.5

14.1

Diptera

Flies

115.6

301.1

90.5

Ephemeroptera

Mayflies

1.1

85.7

151.7

Epiproctophora

Dragonflies

1.5

2.3

0.6

Lepidoptera

Moths

0.6

1.0

0.5

Megaloptera

Alderflies

0.0

0.0

1.4

Neuroptera

Lacewings

0.4

0.0

0.2

Plecoptera

Stoneflies

0.0

0.0

4.1

Trichoptera

Caddisflies

5.7

113.9

46.9

Zygoptera

Damselflies

4.6

6.3

0.6

Insecta

stormflow rise, rate of stormflow fall, and the Base Flow Index (Table 11.2). While this is essentially at odds with that observed in more temperate systems (Walsh et al., 2005; Fletcher et al., 2013), it may not be a surprising result for a subtropical system. Streams in South East Queensland are characterized by a period of relatively dry weather (JuneeOctober) where catchments can become “hardened” and in many respects resemble impervious surfaces, which coincides with the onset of intense storm events at the commencement of summer (NovembereDecember) (Leigh et al., 2013). During this time, flows in all stream types, even streams with extensive upstream forested areas, may resemble the “flashy” pattern of urban streams. This suggests that the endemic fauna of subtropical streams may have both resistance and resilience traits to “flashy” hydrology.

11.4.3 Diversity of EPT taxa The proportion of the insect orders Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies)dthe EPT taxadis a commonly used metric for assessing the health of streams. A higher proportion of EPT taxa suggest “better” stream health (Sponseller et al., 2001). EPT taxa are relatively more sensitive to a range of pressures associated with poor stream health, including habitat degradation, reduced water quality, increased pollutants, and changed hydrological regime, compared with other aquatic macroinvertebrate orders (e.g., Diptera [flies], Odonata [dragonflies],

244 Approaches to Water Sensitive Urban Design

Species richness (S)

40

30

20

10

0 Blunder creek (WSUD)

Stable swamp creek (URBAN)

Tingalpa creek (FOREST)

Site FIGURE 11.11 Box plots (minimum, first quartile, median, third quartile, and maximum) of species richness from all samples collected from each of the treatment streams between June 2011 and May 2012.

Blunder creek (WSUD) Stable swamp creek (URBAN) Tingalpa creek (FOREST)

2.00

Axis 2

1.00

.00

–1.00

–2.00 –2.00

–1.00

.00

1.00

2.00

3.00

Axis 1 FIGURE 11.12 Two-dimensional multidimensional scaling ordination plot based on the BrayeCurtis similarity measure showing the distribution of all samples for the three different streams. Stress ¼ 0.14.

Urbanization: Hydrology, Water Quality, and Influences on Ecosystem Health Chapter | 11

Tingalpa creek (FOREST)

Blunder creek (WSUD)

245

Stable swamp creek (URBAN)

Ephemeroptera leptoph lebiidae (mayflies)

Gastropoda mollusca physidae physa acuta (snails)

Gastropoda mollusca planorbidae (snails)

Gastropoda mollusca hydrobiidae (snails)

Crustacea decapoda aytidae (shrimps)

Crustaces microcrustaceans (water fleas)

Diptera chironomidae tanypodinae (nonbiting midge)

Diptera chironomidae orthocladiinae (nonbiting midge)

Diptera chironomidae chironominae (nonbiting midge)

Oligochaeta (worms)

0

5

10

15

20

25

30

35

% contribution to site similarity

FIGURE 11.13 Taxa identified by similarity percentages procedure as contributing to at least 50% of the within-site variation in assemblage composition.

Coleoptera [beetles], Heteroptera [true bugs]). Hence, the diversity of EPT taxa throughout the sampling period for the three sites was compared. We predicted that the FOREST site would have a higher diversity of EPT taxa than either the WSUD or URBAN streams. Fig. 11.14 shows there was a significant difference between the three sites in the abundance per sample of mayflies (Ephemeroptera: ANOVA F ¼ 6.142; P < .01), stoneflies (Plecoptera: ANOVA F ¼ 8.603; P < .01), and caddisflies (Trichoptera: ANOVA F e 5.150, P < .001). There were significantly more Ephemeroptera and Plecoptera in the FOREST site compared with the URBAN or WSUD site (Fig. 11.14). However, there were significantly more Trichoptera in the URBAN site compared with either the FOREST or WSUD site. The patterns of EPT abundance across the three sites were in accordance with the conceptual model predictions, with a greater number of EPT found in the FOREST site, while the WSUD site had the lowest number, possibly reflecting the poor water quality (low pH and high conductivity) at this site.

11.5 CONCLUSIONS The differences in hydrology found between the sites we monitored can be related to urbanization. The two more urbanized sites, Stable Swamp Creek (URBAN) and the Blunder Creek tributary (WSUD), had a larger number of flow rises across nearly all months, higher average daily flows, and greater rates of fall during flow recession. The one parameter that was markedly different to that expected was the minimum daily flow and the BFI. The URBAN site had continual baseflow throughout the year, which may assist in diluting some of the extreme water quality changes associated with urbanizationdsuch as increased nutrients and organic matter loads (Walsh et al., 2007). The cause of the continual baseflows at the Stable Swamp Creek site is unclear. However, there is a wetland not far upstream from the gaging station, which may act like a “sponge” holding water during wet times and continually releasing it downstream during periods of low flow within the stream (see Leigh et al., 2010). Overall, using a suite of hydrological metrics, the hydrological changes imposed by the upstream WSUD intervention on the Blunder Creek WSUD site appeared to contribute to it being much more similar to the FOREST site than the URBAN site.

246 Approaches to Water Sensitive Urban Design

Plecoptera abundance per sample

Ephemeroptera abundance per sample

30

600

400

200

20

10

0

0 Blunder creek (WSUD)

Stable swamp creek (URBAN)

Tingalpa creek (FOREST)

Blunder creek (WSUD)

Site

Stable swamp creek (URBAN)

Tingalpa creek (FOREST)

Site

Trichoptera abundance per sample

1000

800

600

400

200

0 Blunder creek (WSUD)

Stable swamp creek (URBAN)

Tingalpa creek (FOREST)

Site

FIGURE 11.14 Species richness box plots (showing minimum, first quartile, median, third quartile, and maximum values) for the abundance per sample of (a) Ephemeroptera, (b) Plecoptera, and (c) Trichoptera for each of the stream types.

As with the hydrological metrics, there were clear water quality differences between sites that could be related to urbanization. Stable Swamp Creek (URBAN) and the Blunder Creek tributary (WSUD) had larger daily ranges in DO and temperature. The higher conductivity and low pH observed at the WSUD site were likely associated with groundwater inputs containing dissolved Fe, which can cause both acidification and salinization of stream waters (Singer and Stumm, 1970). The multiple water quality impacts at the WSUD site, including high daily ranges in DO and temperature, high conductivity, and acidic conditions, make it a harsh environment for macroinvertebrates. Water quality parameters in the forested Tingalpa Creek, although occasionally harsh, are more often typical of forested catchments, with low daily ranges in DO and temperature and low conductivity. Species richness was similar across all three sites, but assemblage composition was significantly different, suggesting that each stream type supported a distinct assemblage characterized by species presence and abundance. Stable Swamp Creek (URBAN) and the tributary to Blunder Creek (WSUD) were dominated by taxa common in degraded streams, including midges (Chironominae) and worms (Oligochaeta) (Suren and McMurtrie, 2005). In comparison, the Tingalpa Creek (FOREST) assemblage was dominated by mayflies (Ephemeroptera: Leptophlebiidae) and other insect ordersd more typical of headwater streams with good riparian and catchment cover (Sponseller et al., 2001; Sheldon et al., 2012a). When the assemblage data were compared with the water quality and hydrology data, it was stream electrical conductivity, daily temperature range, and aspects of hydrograph shape (rates of rise and fall) that explained the most variation in assemblage differences between the three sites. These physical parameters are known to be heavily influenced by urbanization (Walsh et al., 2005; Hawley and Vietz, 2016).

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247

Many of the biological and water quality differences observed between the FOREST site and the WSUD and URBAN sites could be attributed to the hydrological impacts of urbanization. In nearly every parameter measured in this study, the WSUD site was more closely aligned to the URBAN site than the FOREST site. Given the known negative impacts on stream systems that even small areas of upstream impervious area (>10%) can have (Walsh et al., 2005), the manner in which WSUD is implemented will have a major impact on the ecosystem health outcomes for stream ecosystems. If the implementation of WSUD measures focusses completely on hydrology without also considering water quality impacts, the ecosystem health outcomes for the stream may not be realized. In summary, our results suggested that there were strong differences in the hydrology, water quality, and macroinvertebrate assemblage between the FOREST site and the two sites impacted by urbanization. Despite the upstream WSUD development of Forest Lake, both the WSUD and URBAN sites had hydrology characterized by high rates of rise and fall of flood peaks. However, very little variation in macroinvertebrate composition across the three streams could be directly attributed to the monthly hydrological metrics calculated from the daily data. Rather, DO, pH, and temperature range explained more variation, suggesting that even when measures are implemented to help restore natural flow patterns, poor water quality can have an overriding influence on stream ecosystem health. The WSUD site measured in this study was implemented in the 1990s suggesting that the stream ecosystem should have had plenty of time to adjust to the new conditions, notwithstanding reports that the “ghost of land use past” can have a significant impact on contemporary stream conditions (Maloney et al., 2008). The Forest Lake area was a cleared agricultural catchment before urbanization, and the initial clearing most likely contributed to the significant stream incision, which is adversely impacting stream conditions today. To fully understand the role of WSUD developments in restoring streams, ecosystem health studies should cover the full range of WSUD types and control for factors such as prior land use, upstream riparian cover, and unique changes in the groundwater hydrogeochemistry.

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