Little Stringybark Creek project: assessing hydrologic impacts of alternative techniques through a flow metrics analysis SCHMITT Vincent. Supervised by FLETCHER Tim, BARRAUD Sylvie, BURNS Matt and SAMMONDS Mike.
Abstract With human land use over several decades and traditional stormwater conveyance systems, many former natural streams are now deeply altered. Changes occur on multiple aspects modifying together the flow-regime, water quality and aquatic conditions. After assessing these transformations, new stormwater managements have emerged. Following this tendency, recent projects tend to retrofit urban catchments and highlight improvements at the outlet. Thus, Little Stringybark Creek project aims to restore a degrade stream by implementing alternative techniques throughout the site and mimicking a natural water-cycle. To estimate their performance, other urban or natural watersheds are used as a comparison. The current analysis is a first stage in this larger program. It considers flow metrics resulting from continuous rainfall and flow data over 6 years of record. Such a study usually requires more than 10 years to assess reliable improvements but an early review can still be seen as a predictor. Under this hypothesis, we explored different flow indicators to examine the main differences among all sites. We found that Little Stringybark Creek still faced together strong high-flow, great flashiness and weak baseflow contributions. Understanding these critical features, specific metrics were identify to quantify oscillations and intermittencies of the flow, but didn’t show improving trends. Another approach based on isolated rain-flow events then focused on stormwater runoff volumes. Surface runoff remains indeed a main driver to restore the water balance while effective impervious areas are thought to be overriding in this mechanism. Using a semi-automated method, we thus assessed the runoff coefficient for more than a hundred similar events. The results seemed to show an improvement even if independent works tend to attenuate its degree of reliability. We conclude that the results don’t ensure for now beneficial impacts on the flow-regime linked to the new stormwater management. More data are still needed to extend the analysis while limitations appeared when trying to compare urban sites with natural ones. Because of very different behaviours, a unique events-based method seems hardly feasible. Nonetheless, we assume that the present study can serve as a base for further works in this particular project.
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1. Introduction With a fast urban sprawl since the 1950’s, peripheral landscapes features have deeply changed around cities. These new alterations encountered in many countries are often defined by a scattered urbanization and an increase in imperviousness. With new buildings, roads and less vegetation, the changes in land cover also modified the natural water cycle and flow-regime of the creeks. Because of increasing overland runoff and flood risks, a concern for stormwater managements has grown in the same time. For decades, the drainage systems were designed to channel the stormwater quickly and directly to a common outlet. Newly created impervious areas were then connected to the pipe conveyance network. More recently, this traditional approach has been criticised for its impacts on both the ecology and hydrology of the rivers. Bad consequences encompassed all features of the flow-regime. With much less pervious and vegetated surfaces, amount of direct stormwater runoff tends to increase whereas the infiltrated fraction is getting weaker. The groundwater recharge is therefore usually reduced and low flow spells harsher. On the other hand, drainage-efficiency techniques combined with a drop in vegetation cover (i.e. evapo-transpiration outputs) convey runoff water to the receiving waters, quicker and in a greater proportion. Typically, streams become flashier with stronger and more frequent flow peaks occurring on shorter times (Olden & Poff, 2003 ; Kennen & al. 2011 ; Burns & al., 2012). Accentuated by degraded stormwater quality at the outlet, those transformations also modify other aspects of the creek: geomorphologic features with greater channel erosion, chemical properties with higher PH, temperature and pollutants rate, or biological degradations. In order to overcome those issues, new approaches have focused on restoring natural
water cycle considering surface flows, interflows, baseflows and treating stormwater locally rather than implementing ‘end of pipe’ methods (Petrucci, 2012). Projects have thus been set up to monitor site-scale stormwater management efficiency. By implementing new storage devices, disconnecting impervious areas and promoting infiltration, original flow regime is expected to be restored in every aspect: high-flows, low-flows, etc. To assess this trend, a wide range of hydrologic metrics has already been viewed in the literature (Olden & Poff, 2003 ; Kennen & al. 2011). Most of them are known to show correlations with ecological conditions, and therefore seem to be also good predicators of environmental health. Under the assumption that to enhance urban stream behaviour, improving water quality without restoring natural flow regime isn’t enough, a selection of flow metrics is determinant. Furthermore, to evaluate any significant tendency in those metrics, comparisons must be made with different sites involving urbanized catchments and degrade rivers or natural sites with healthy creeks. The method must finally allow identifying changes due to new stormwater management techniques. Being part of a much larger project, this study doesn’t aim to be exhaustive but to qualify flowregime patterns for different sites and assess the first signs of improvement in hydrologic metrics. It focuses on Little Stringybark Creek which experienced retrofit works between 2007 and 2014 to modify his drainage system from a conventional management to a flow-regime management (Burns and al. 2011). Aiming to restore the natural flow features, several nearby undeveloped catchments served as hypothetical references for healthy flow patterns. Furthermore, similar residential areas with traditional ‘drainageefficiency’ systems were used as control sites, sharing same degrade river behaviours than Little Stringybark Creek before 2007. The common hypothesis is to observe a temporal trend in Little Stringybark Creek hydrology after all the retrofit. Indeed, in order to assess the new stormwater 2
management efficiency, flow-regime features are expected to drive away from those of the control sites and approach natural creeks values. We demonstrate in this study that such a trend isn’t clearly detected yet. Some indicators like the runoff-rainfall ration start to show improvements whereas time-series metrics don’t present good patterns. Further investigations are still to be led to compare all the sites, while more years may be needed to assess reliable tendencies for Little Stringybark Creek.
2. Methods 2.1. Overview Our analysis focuses on catchments which showed good data for both flow and rainfall at the beginning of the study. With this priority, several streams were chosen: main stem of Little Stringybark Creek for the ‘impact’ site, Brushy and Ferny Creeks as two ‘control’ sites and Lyrebird Creek as a ‘reference’ site. Even if some of them have been monitored since 2001, datasets are known to be accurate for all catchments after 2009. Therefore, this study considers only 6 years for both rainfall and streamflow data in order to compare each stream. It’s well known that such analysis based on time series metrics need to encompass more than 10 years of data before assessing reliable trends (Olden & Poff, 2003). Nonetheless, an early comparison between creeks could provide some insight into first changes of the flow-regime at the impact site. The process is so divided in three parts. We first approach differences in flow patterns by means of the flow duration curve, often used as an easy tool to visualize these alterations (Kannan & Jeong, 2011; Petrucci & al. 2013). Otherwise, we decided to estimate annual and seasonal flow-regime because natural dynamics continuously vary along the year. On one hand, rainfall characteristics differ according to the season with usually more intense and scattered episodes in summer than in winter. On the other hand, cooler temperatures
and higher wetness conditions around winter are likely to decrease evapo-transpiration and infiltration efficiency. Behaviours of pervious and impervious areas will subsequently be modified. The availability of data among the sites drove us first to define seasons in quarters of year: summer from January to March, autumn from April to June, winter from July to September and spring from October to December. Later works based on meteorologic seasons testified the same trends for the different flow metrics. Overall, this approach allows us afterwards to identify critical changes or timings for the flow-regime and choose several metrics mainly related to the flashiness or low-flow aspects. These indicators based on the time-series dataset are finally completed by others resulting from ‘storm-events’ identification. This last stage requires a method to select dependent rainfall event and streamflow response. It also provides a first quantification of stormwater runoffs from effective impervious areas (EIA) and an estimation of any decreasing tendency. Former reports suggest that such a trend is necessary to mimic undegraded flow-regime (Walsh & al. 2012).
2.2.Sites description All the sites are located in the Dandenongs, a set of low mountain ranges located on the urban fringe of Melbourne, Victoria, Australia, approximately 37 km east. They share the same usual wet climate with annual rainfall between 1000 - 1500 mm (www.bom.gov.au). Impact site Little Stringybark Creek is mainly constituted of residential areas with low to average density. Clay soils with weak permeability are the most common. The study largely focuses on the main stem (LIS4) and its watershed, a 4.23 km2 area with 9% of connected imperviousness estimated. Otherwise, three tributaries exist and correspond to different sub-catchments features: the north tributary or LSN, mainly rural (1.5 km² area for 5% of connected imperviousness) 3
the central tributary or LIS1, residential (0.7 km² area for 19% of connected imperviousness) the south tributary or LSS, residential (0.98 km² area for 11% of connected imperviousness)
Most of the catchment was originally drained by traditional sewers and pipe conveyance directly to the stream. By the end of 2013, the Little Stringybark Creek restoration project had retrofitted the site by implementing alternative techniques and disconnecting impervious surfaces from the network. Thus, site-scale and allotmentscale interventions were built over the years: water tanks, rain-gardens, swales, retention basins, etc. Control sites For this analysis, the selected catchments present a similar density with Little Stringybark Creek and still drain to conventional drainage systems.
Ferny Creek (i.e. FER) with a 6.42 km² watershed for 9.5% of connected imperviousness and located 17 km south west of Little Stringybark Creek main stem Brushy Creek (i.e. BRS) with a 14.79 km² catchment and 19.6% of connected imperviousness, located 9 km west
The receiving waters are characterized by common flow alterations and poorly ecological conditions with Little Stringybark Creek before any retrofit. Their flow-regime assessment is thus used to separate eventual trends in the impact site consecutive to the new stormwater management from external reasons such as climate variations. Reference site The reference stream is Lyrebird Creek (LYR) with a catchment area of 7.24 km². It’s located approximately 6 km south from the impact site. Most of the coverage is forested with almost 0% of imperviousness directly connected to the stream. The watershed is thus considered as natural with
an unregulated stream. Here, flow monitoring aims to target patterns of a ‘healthy’ river and behaviours for Little Stringybark Creek to mimic. Rainfall and flow gauges The streamflow-regime was assessed in every site and based on flow gauges implemented in each creek. Rainfalls across the catchments were monitored using the nearest rain gauges. Two different sets were used for this study. Melbourne Water gauge 2296901 (Olinda Creek at Mount Evelyn) served a first comparison of flow metrics between the selected sites. Because its localisation was found to be not seemly for each catchment, two others were then necessary: Melbourne Water gauge 586177 (Monbulk Rain Gauge at Silvan Reservoir) for the rainfall in Lyrebird catchment University of Melbourne Water gauge for the rainfall in Brushy catchment Other gauges exist in the area, and further works should apply Thiessen polygons to estimate accurate rainfall events within each catchment. Nonetheless, even if the rain spatial dispersion is yet to be specified and period of record to be widen, this analysis includes enough data to assess a first range of flow metrics in all sites.
2.3.Data analysis Datasets The whole analysis is computerized using R software, and all graphics are produced with ggplot2 package. After a prior calibration and quality code rating of the data, both rainfall and flow analysis rely on 6 min time-step data-frames. Missing values are still occasionally faced but remain rare and scattered. The choice was made to remove these values rather than apply linear interpolation when used for time-series metrics. In order to compare each creek, discharge data were also normalized by catchment area and expressed as mm/d. Because of an ongoing data update, the period of record has changed during the process. 4
For the first part of the study, we encompass data from January 1st 2010 to December 31st 2014, whereas the last part has a wider data subset comparing catchments between January 1st 2009 and March 19th 2015. Flow-regime features using the flow duration curves Before using any specific indicators, the choice was made to assess global hydrologic differences and similarities between the impact site and the others. In recent literature, flow duration curves (FDCs) are often considered as an easy tool to qualify them (Kannan & Jeong, 2011; Petrucci et al. 2013; Hamel et al. 2014). By plotting the mean daily flow values on a logarithmic scale against their probability of exceedance in the stream, shapes of the curve inform graphically the range of flows experienced by the creek. An urbanized catchment is thus expected to produce flashy streamflow characterized by quick runoff responses, high magnitude flow peaks and weak baseflow inputs. The FDC is likely to present steep slopes consequent to this wide range of flow values. On the opposite, undeveloped sites are identified by flatter FDC shapes. They mainly result from a greater groundwater recharge by stormwater infiltration and weaker surface runoff contributions to the stream. Furthermore, because low-flow and high-flow regimes are particularly sensitive to human land use, interests tend to focus on the extreme right and left sides of the curve. The low-flow end can for instance provides insight into the ephemeral or perennial stream behaviour and the groundwater contribution. Since this tool doesn’t give any information about the time of occurrence, we decided to assess both annual and seasonal FDCs. All catchments are then superimposed on a same graphic to visualize common patterns or differences in the flowregime. Low flow metrics For such an analysis, a first stage is to select hydrologic metrics which are relevant to describe the main flow alterations. Previous studies provide
again a wide range of indices known to be related to the creek health (Kennen & al. 2007; Richter & al. 1996; Olden & Poff, 2003). With almost two hundred different metrics proposed, a restricted list must be established to allow a practical comparison between streams. Usually, metrics are based on mean daily flow values. In order to accurately describe differences in flow behaviours, a common approach is to answer five criteria: flow magnitude, frequency, duration, timing and rate of change. Nonetheless, we don’t aim to assess the overall flow-regime in this report, but to estimate improvements in aspects judged critical at this point. After interpreting the flow duration curves, it appears that baseflow and flashiness characteristics present the strongest differences between sites. With the hypothesis that groundwater inputs and cease-to-flow spells are essential to restore good hydrologic conditions, we decided to focus the analysis firstly on the low-flow characterization. Following literature about ephemeral creeks (Duncan & Fletcher, 2014), the choice was made to change ‘traditional’ low-flow metrics as Baseflow Index (BFI) for indicators targeting zero flow spells. Indeed, because of these spells occurrences and durations, a metric as Baseflow Index (seven-day minimum flow divided by the mean daily flow) remains null almost every year for Little Stringybark Creek. Consequently, magnitude and duration of these periods are estimated by: Number of days per year with no flow (annual cumulated duration) Annual mean, maximum and minimum duration of those spells Number of discrete events per year (frequency of those spells) In urban sites, cease-to-flow periods are expected to be greater than for natural sites because of a strongly lower baseflow and quick runoffs. Tough, we take the view that no leaking pipes or different sewage effluents skew the results. On the impact 5
catchment, decreases in effective impervious areas (EIA) and subsequently increases in water storage and infiltration should produce less flashy events and more sustained low-flows. Odds of reducing flow intermittency over the years are thus expected. A peculiar attention is paid for critical dry seasons like summer. Otherwise, because of the greatest incertitude for extreme low-flow measurements, the threshold defining drought events varies. Several values are suggested in different reports as a flow nominal rate or a percentile flow: (Tallaksen & Lanen, 2004). Because of the recurrence of such spells in LIS4, we decided to set this threshold to 0.1 ML/d for each creek in order to encompass the same loggers’ accuracy. Such a value can also describe extreme low-flow features. Finally, timing of the driest periods remains another aspect to focus on. Correspondence between critical hydrologic events and biological calendar is indeed a major issue since flow-regime seasonality is likely to be disturbed with urbanization. We chose to assess dry conditions on long period using minimum monthly flow rather than dating the minimum seven-day spell in a year (Olden & Poff, 2003; Duncan & Fletcher. 2014). Indeed, the annual presence of multiple long events with zero flow in the impact site would provide us several dates. Thus, both magnitude and timing of the minimum monthly flow were estimated for each stream. Again, any improvement for Little Stringybark Creek consists in approaching Lyrebird’s values. Flashiness metrics Since previous FDCs analysis suggests that LIS4 still experiences a wide range of flow values, flashiness assessment is relevant. Even for weak runoff events, urban streams have quick responses in water level with higher peak flows. Indeed, overland flows tend to be more important and conveyed quicker to the receiving waters. With extreme low-flows in summer, such a disturbance may be even more critical. These alterations
impact the aquatic ecosystem and the stream geomorphology often changing the channel shape, aquatic habitat and species distribution. To compute such an aspect of the flow-regime, two indices are frequently selected among all the metrics available. First, Tq,mean estimates the annual fraction of time that the mean daily flow is higher than the mean annual flow (Konrad & Booth, 2002). This provides information about the peak flows duration. Hydrographs for urban streams have usually quicker rising and falling limbs with a weaker baseflow contribution, resulting in a smaller Tq,mean value. On the opposite, the Richards-Baker Flashiness index (R-B index) should be higher. This last metric describes the flow oscillations relative to the total flow over a year as: sum of the absolute values of change in mean daily flows divided by sum of the mean daily flows (Baker et al. 2004). Assessing daily variations, it remains a stable metric even on a short period of record and allows us to describe short-term changes in stream flow. For this study, Tq,mean and R-B Index are computerized for each year and season in order to highlight any sensitive period. Event-based runoff volumes As mentioned previously, urbanization tends to increase the volume of surface runoff delivered to the creek. Nonetheless, rainfall events through urban catchments can produce different inputs for the runoff. After a small initial loss, effective impervious lands experience the first surface flow and then followed by all the imperviousness. Finally for larger episodes, saturated pervious soils tend to contribute too. Each flow response consecutive to an event can thus be divided in different parts: a ‘quick’ flow resulting from direct runoff, an ‘inter’ flow from delayed subsurface reactions and a ‘slow’ flow due to groundwater inputs. Several papers take the view that reducing the runoff volume is indispensable to restore any pre-development water-cycle or aquatic environment (Walsh et al, 2012; Burns et al. 2013). Prior works also highlighted a prevalence of the 6
connectivity of impervious lands in this mechanism. According to that, the retrofit program on Little Stringybark Creek widely aimed at disconnecting impervious surfaces with 13 km2 of treated area by the end of 2014. Therefore, in this study the choice was made to focus on specific events for which quick flow and direct runoff from EIA would prevail. We used runoff coefficients based on those episodes rather than daily values over the period of record (Blume & al. 2010). In the same time, it allowed us to separate flow responses from their antecedent conditions. The absence of EIA in LYR and apparent missing values for FER in the early stage prompted us to reduce the subset of catchments to Brushy and main stem LIS4. Most of the effective imperviousness being treated after 2013 in LIS4, a trend is expected to appear from this date. Overall, the method consists in two parts. Firstly, rain and streamflow events which are related must be selected. Existing subsets of isolated rainfalls were used with Brushy and Olinda gauges. A visual examination of the hyetograph and its matching hydrograph had allowed a first delimitation of independent rain-flow episodes. If a flow recession curve overlapped with a following rainfall, the two rain events were merged in one spell. Afterwards, additional parameters are respected in order to have a consequential and homogeneous subset and reduce events variability. They aim to target runoff spells as the result from the fraction of connected impervious areas. To ensure it, are only selected rainfalls with small depth, short duration and a relative dry antecedent. Following these criteria, we first minimize the odds of other impervious or pervious contributions and the fraction of interflow and slow flow inputs. At the same time, it usually matches ‘clear’ rain-flow events with lumped peaks and so eases the
identification of start and end. A first range of binding values was used but didn’t encompass enough events for each year. In order to observe a temporal trend, the final subset was widened to consider 113 events with:
Rain depth between 2 and 25mm (episodes of less than 2mm being too instable) Rain duration less than 17 hours No other rainfall during the previous 24 hours
Moreover, we assessed rain and flow data qualities while recording wetness conditions by summing rains depth over the preceding 10 days. It must allow us to remove any peculiar event if necessary. Secondly, streamflow pulses were identified in two stages. According to several papers (Blume et al. 2010; CSIRO & SKM, 2010; and other), we used a digital baseflow separation. This approach was based on the Lyne and Hollick filter which is fluently present in computer packages. Using Hydrostats Package (Nick Bond, 2014), baseflow indices are calculated to assess baseflow contributions to the stream for the complete timeseries (Figure 1). Default alpha value operating in the filter was shifted from 0.975 to 0.995 to avoid overweening high indices during flow peaks.
Figure 1 OLN Hyetograph & LIS4 Hydrograph – Flow events identification. Red and blue dashed lines record start and end times of a rainfall. Blue line represents continuous baseflow inputs. Black dots are the total flow. Green dots show stormflow events when the baseflow falls beneath 95% of the total flow.
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Following this computerized method, we then used a nominal baseflow value to identify the start and end of a flow response. The ratio between baseflow and total flow was thus calculated for each 6 min time-step and the threshold was set at 0.95. Thus, an event begins when baseflow inputs contribute for more than 95% to the stream and stops when they fall beneath. Applying the same criteria for each catchment aims at curbing the identification expediency from a manual approach. By then plotting on a single frame corresponding rainfall and streamflow events, information as rain depth and flow volume are calculated. Nonetheless, a final manual step was necessary to audit each event and specify its start. Indeed, the onset of the rising limb was often visible before BFI reaches 0.95 and some extraneous rainfall could occur during the recession curve. The whole process is summarized in Figure 2 and 3. Having this subset of events, temporal relationship was then assessed between rainfall and runoff. Runoff coefficients from connected imperviousness were estimated as the event runoff depth (i.e. removing baseflow volume to the flow volume and dividing by catchment area) on the rain depth. As it was said before, trends in rainfall-runoff relationship give an insight into zater-cycle changes between different sites. With a decrease of EIA in LIS4, the outlet is expected to suffer less frequent flow pulses and less amount of runoff for this category of events.
Figure 2 Method for the rain-flow events identification
Figure 3 Examples of selected events for LIS4 – Hyetographs and hydrographs
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All metrics presented previously are reminded in Table 1. Flow-regime feature
Metric
Definition
Units
Expectation trend for improvement
Reference
Low-flow Magnitude
Annual value with calendar year No-flow spell cumul
Cumulative sum of days with no flow
Days
Decrease
Duration
No-flow spell range
Mean value as the number of no flow days divided by the number of spell occurrences Maximal and minimal duration
Days
Decrease
Duncan & Fletcher, 2014 Duncan & Fletcher, 2014
Frequency
No-flow spell count
Number of times each year that discrete no-flow Count spells occurred
Decrease
This study
Timing
Minimum monthly flow
Month with the minimum average monthly flow Date
Magnitude
Minimum monthly flow
Value of the minimum monthly flow
mm/d
Match reference site timing Increase
Duncan & Fletcher, 2014 This study
Flashiness Rate of change
Annual value with calendar year R-B Index
Sum of daily flow oscillations in absolute value divided by sum of the daily flows
No units
Decrease
Baker et al. 2004
Duration
Tq,mean
Runoff Magnitude
Events mainly involving EIA Runoff coefficient
The fraction of time during a year that the mean Fraction of Increase daily flow rate is above the mean annual flow year
Konrad & Booth, 2002
Runoff depth without baseflow contribution divided by rainfall depth, for each event
This study
No units
Decrease
Table 1 Summary of flow metrics selected to compare Little Stringybark Creek with other catchments.
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2.4.Results Flow Duration Curve to qualify flow-regime patterns
Figure 4 Annual Flow Duration Curve for catchments. Brushy in red, Ferny in orange, Little Stringybark main stem in blue and Lyrebird in green. The dashed lines are the thresholds (10 & 90 flow percentiles) used to separate high-flows and low-flows. Flows normalised by catchment area to L/hr/m2 (or mm/hr). Logarithmic scale on y-axis.
For each urban site (i.e. Little Stringybark Creek, Brushy and Ferny) hydrological changes in the overall flow-regime remain visible over the years (Figure 4). In comparison to Lyrebird as the undeveloped site, flow duration curves have a steeper shape, meaning that the creeks still experience a wide range of flows. Below the 10th percentile, a flatter slope reports that a common trend occurred with a decrease in high-flows range and maximal magnitudes. Nonetheless, among all
the sites LIS4 presents ‘weaker’ low-flows (i.e. above 90th percentile) and its steep curve informs of scarce groundwater contributions. After improvements in 2011 and 2012 seemingly due to a wetter climate, it shows again flow intermittencies and little sustained low-flow spells. On the opposite if we set aside 2010, the small inter-annual variations in Lyrebird’s FDC and its horizontality testifies to the stability of a ‘natural’ flow-regime. Otherwise, common highest values for Ferny along 2010 and 2011 make difficult any comparison; several inaccuracies in the flow data were latest found and induced us to lay aside Ferny for the rest of the study. On figure 5, the seasonal flow duration curves precise those results. Summer remains the period with the strongest inter-annual variability in each creek. It’s also the season where the overall flowregime presents more differences between LIS4 and the others. Its low-flows are particularly affected while all the cease-to-flow spells occur during this time. Singular dry episodes for LYR in summer 2010 could suggest an ephemeral behaviour in natural creeks depending on the season. On the opposite, FDCs shapes tend to become more similar in winter and steadier after 2011, so that general alterations are proportionally less important. A common pattern for urban sites is also a decreasing magnitude of high pulses but a standing strong gradient (i.e. still flashy flow responses). Noticeably with similar seasonal variations or FDCs shapes from autumn to spring and even for low-flows, Brushy and Little Stringybark creeks share comparable flow-regime features. Defined by a great flashiness and recurring extreme low-flows, summer remains an hazardous period for both of them.
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Figure 5 Seasonal Flow Duration Curve for catchments. Flows normalised by catchment area to mm/d. Logarithmic scale on y-axis.
Figure 6 & 7 Number of zero flow days per year – Mean, maximal and minimal duration of zero flow spells per year. Cease-to-flow periods set below a 0.1ML/d threshold.
Low-flows metrics to assess critical alterations As the FDC approach suggested it, the low-flows face greatest issues among urban catchments. Figures 6 and 7 show that once again, LIS4 experiences greatest cease-to-flow spells than the other creeks throughout the years. Neither magnitude nor duration criteria improve. With a drop in 2011 and 2012, further years experience frequent episodes with a great range of durations. 2010 presents for instance 63 days with zero flow against 60 in 2014. Even if the 0.1 ML/d threshold may be too loose, it still describes extreme lowflow spells. Between a recurrent mean duration of 5 consecutive days and a maximum in 2014 of 28 days, LIS4 stands widely above the other streams. Indeed, we know that LYR only experiences spells in 2010 (78 days for a mean duration of 7 days) whereas FER faces no such events. With few episodes over 2011 and 2012 ephemeral behaviour doesn’t appear consisting in BRS.
For the minimum monthly flow, LIS4 and BRS share again similar patterns (Figure 8). Except for 2011, they indeed face a same timing. They also have the closest magnitudes relative to the others, ranged between 0.54mm/d and 0.04mm/d (on average 38% of relative difference but an increasing gap since 2013). Even if LIS4 shows values more than twice as high as LYR with a same dating before 2011, the trend changes during the following years. Since 2012, the impact site experienced indeed drier months and/or earlier timing. Its magnitude remains the lowest of all catchments (in average 60% of BRS and 34% of LYR values), resulting from very weak low-flows and long zero flow spells. Nonetheless, the seasonality of occurrence keeps up in summer either for LIS4 or LYR and BRS. Among the urban sites, Ferny creek has again singular features. With a mean value of 0.46 mm/d, the minimum flow is thus always the highest and happens on a wider range of dates over spring and summer.
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Figure 8 Minimum monthly flow per year for each catchment. Timing and magnitude. Flows normalised by catchment area to mm/d
Flashiness The R-B Index results on Figure 9 are stable for most of the streams. FER is the exception varying from 0.26 to 0.98. As expected, values for the reference site are strongly inferior: on average 6 times below Little Stringybark and 7 times below Brushy. Steady high values near 0.9 tend to show that the impact stream still experiences frequent and rapid short-term changes. Evolution in ratios between it and the others isn’t either significant. Because of the limited number of years, no statistical trend analysis was assessed. Seasonal approach informs that summer is more likely to accentuate the gap between LIS4 or BRS and LYR. It matches the original hypothesis that pervious and vegetated areas efficiency is also impacted by seasons.
Figure 9 Annual R-B Index for each catchment.
Tq,mean graph (Figure 10) provides a same trend. With steady values respectively 0.22 and 0.20 on average, LIS4 and BRS remain two times beneath LYR (around 0.41). Thus, exceeding on average its annual mean flow during just 20% of the year, Little Stringybark Creek still exhibits high flow peaks with rapid recession curves. Nevertheless, this stability tends to be a good feature in balance with what could appear as a decreasing trend for Lyrebird after 2011 and for Ferny.
Figure 10 Annual Tq,mean for each catchment.
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Runoff volume decrease as a preliminary Among the 113 events, a few were removed afterwards because of missing data or episode quality. With 107 events for LIS4 (i.e., 5.3% of the original subset set aside) and 101 for BRS (i.e., 10.6% of loss), the final subset for each creek is still consequent to establish a comparison. Visual examination of the runoff coefficient plots for each site (Figure 11 and 12) indicates strong variations of the watershed response according to the seasons. Such seasonality results in greater coefficients over spring and winter which are partly explained by the antecedent wetness condition tending to be higher. It means that initial water storages are less likely to happen while saturated soils can eventually participate. Besides, with the assumption that most of the time EIA only contributes, the influence of rainfall depth is also expected. It’s mainly perceptible on BRS graph for which the strongest rains lead to coefficients outlining the others.
Figure 11 Events runoff coefficients according to the rainfall depth for BRS and LIS4 over 1926 days between 2009 and 2015. Colors gradient represents the rainfall depth ranged between 2 and 25mm.
Overall, a trend seems to be visible for LIS4 while less event runoffs outmatch 10% of their rain and a greater fraction remains around 5%. For a same prior dry condition (less than 10mm previous rains), storms weaker than 5mm appear to generate less runoff along the years, matching the decrease of EIA throughout the watershed. On the opposite, BRS doesn’t show any trend. Its runoff coefficients seem to be only dependant from rain magnitude and prior conditions. Nonetheless, it’s important to notice than because of singular rainfall conditions in 2014, few events fit our subset of criteria. Being the last complete year recorded for our analysis, skewness in LIS4 tendency is feasible. Moreover, independent works attempted to reveal breakpoints in the runoff-rainfall relationship after 2013 without any success. Many more years of data still seem required.
Figure 12 Events runoff coefficients according to the antecedent wetness conditions. Colors gradient represents the cumulative rainfall depth over the 10 previous days.
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2.5.Discussion Improvements in extreme low-flows: a critical feature in disturbed flow-regimes Using an FDC approach, we showed that several aspects of the flow-regime are altered by lands urbanisation, and especially for high-flow or lowflow spells. While the gap between urban sites and the reference one seems to decelerate for the high-flow regime, the impact site remains with critical low-flow features. Literature widely described its consequences on both water quality and aquatic biotope. A subset of flow metrics from Olden & Poff and Duncan & Fletcher studies allowed us to quantify this trend. It appears that seasonality of extreme low-flows isn’t much shifted (i.e. driest months happen sooner or later in summer) but more their magnitude and frequency. For instance, LIS4 still experienced in average 50 days of those spells per year in 2013 and 2014. With 22 consecutive days at the utmost, their long durations are another way to highlight the weakness of baseflow contributions. This concern is intensify since we showed that the flashiness metrics remain steady (RB Index close to 0.9 and Tq,mean around 0.22) and follow the same pattern as Brushy Creek. Nonetheless, with many retrofits to enhance infiltration dynamics through the catchment, improvements in the groundwater flow would be expected. Since the period of record is finally undersized to assert any definitive trend, we have to put the results into context. These current values may be a consequence of other processes:
The time-scale of groundwater recharge can explain a delay between retrofits implementation and any impact on the baseflow recharge A great amount of infiltrated water may move downward a deep aquifer and never contribute to the creek Aspects of the stormwater management as storage-irrigation can result in shallow interflows more than deep percolation
The presence of a karst hydrologic system (Vázquez & Suné, 2003; Pellerin & Wollheim, 2007) may route infiltrated water through the subsurface quickly to the stream or even to another outlet. Several studies introduced this concept of ‘urban karst’ as a wide buried system consisted of pipes connected networks which intercept subsurface stormwater. Hence, they tend to create preferential pathways to the detriment of slow flows to the groundwater.
This overview shows once again the complexity of hidden baseflow dynamics and finally of low-flow analysis. Variations in spatial extent of groundwater flows and oddities in the geomorphology or urbanisation of each catchment make it difficult to generalize. On-going studies by Jeremie Bonneau focus on the baseflow contributions. Using chemical and isotopic tracers, they tend to assess those mechanisms. The subsequent hydrograph separation is used to identify stormwater interflows across the subsurface from inputs due to the groundwater recharge or quick surface runoffs. Quantify event runoff following a drop in EIA and infer improvements in other flow metrics According to several studies (Walsh & al. 2012, Hamel & al. 2014) we assumed that a drop in runoff volume is mainly led by the EIA and remains a prior to restore other flow-regime features. Considering these hypothesis, the hydrograph separation seems to be also significant in relation to our events approach. The decreasing trend that tends to appear for the direct runoff coefficients in LIS4 may be a bit premature but shows an improvement in the catchment behaviour. On one hand, the creek always reacts for the same range of small rain events but less ‘strongly’ (i.e. a runoff coefficient lower). On the other hand, it is still different from Lyrebird stream in which flow pulses usually start after much stronger rainfall. Though, the current results don’t allow us to generalise a proportionate decrease in runoff volume for larger 14
events. Constancy of flashiness metrics tends to reveal the wrongness of such an assumption and we saw previously that hidden dynamics can be involved. Because it aims to focus on events implying only effective impervious during relative dry conditions, this study considers no leaking pipes and overland flows as the main inputs, so a linear relationship between rain and runoff. It remains a first approach for the analysis while a second stage would wider the subset of events to implicate the total impervious area and even pervious surfaces. Considering larger events require to encompass a new range of aspects such as the evapotranspiration outputs, the storage capacity trough the catchment, irrigation or the non-linear soil characteristics. With all these components, a better understanding of the non linearity in rainfall-runoff transformation seems to be necessary. Thus, it would greatly benefit from the hydrograph separation to set aside surface runoff from subsurface flow, interflow from baseflow for different types of events. Indeed, a drop in the EIA and some consecutive improvements in runoff volume could be mitigate by an increase in interflows and prevent a proper restoration of natural baseflows. Hence, an eventual decreasing trend in runoff coefficients may happens while flashiness and extreme low-flows persisting at LIS4. While attempting this larger analysis on a much important subset of events, a regression approach between runoff and rainfall could be undertaken. Literature provides examples of such a method which assess runoff coefficient and corresponding effective imperviousness through a catchment (Boyd & al. 1994; Brett & Allan, 2014). The suggested approach implies identifying breakpoints on the runoff-rainfall graph (i.e. where gradient of the regression line changes). Slopes of the different segments assess which part of the catchment are actually involved in the runoff: ‘effective impervious’, ‘total impervious’ and ‘total impervious + pervious’.
Study limitations and implications for further analysis This study takes part in a more substantial project and remains a first stage in its flow data analysis. A main objective was to explore and qualify together methods and eventual trends for hydrologic features. It also provides a first quantification of several flow-regime alterations compared to other urban or natural streams. Thus, the framework based on the studies by Olden and Poff (2003) was useful; it allowed us to easily select a restrained subset of flow metrics significant for the baseflow and flashiness of the creeks. Nonetheless, this approach faced some issues. Intermittencies in the streamflow forced us to transform several metrics according to the study by Duncan and Fletcher (2014). Then, the limited number of years throughout the period of record (i.e. less than 10 years) prevented proper statistical analysis on the data. This worsened by the lack of clear patterns or trends in LIS4 metrics, except the steadiness of its flashiness. The events runoff quantification is thus a complementary approach. Indeed, runoffs are implied to be a main cause in flow-regime changes, and it allows us to overcome the absence of trend in short length time-series metrics. Nonetheless, some limitations appeared too. Mainly, the method seems more reliable for urban sites than for natural watershed. The rainfalls identification was based on a visual examination of the hyetograph and comparisons with flow pulses. The imprecision remains relative for urban catchments which share similar peak flow features but becomes quite subjective when compared to natural sites with flow pulses strongly different in shape, frequency and delay. An automate method is thus advisable to select independent rain events. Literature shows several ways to identify them from time-series data (Joo & Lee, 2013; Hamel et al. 2014). The inter-event time (EIT) is often used and represents the minimum dry period between two rainfalls assuring their independency. His
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value is most of the time ranged between 3 and 12 hours. Another issue hides in the identification of flow responses. The choice of a computerized approach using a baseflow ratio (i.e. baseflow divided by total flow) was made to minimize subjectivity in this selection. Useful for streams having common weak baseflow and important runoff volume (as LIS4 or BRS), the unique ratio threshold of 0.95 makes rapidly difficult the comparison with undeveloped sites. Since they usually experience elevated groundwater inputs, this last criterion often removes a fraction of the flow events and struggle to describe a ‘real’ flow response. In addition, longer recession curves tend also to create dependency between each episode. Finally, the stronger evapo-transpiration in a natural catchment provokes clear diurnal flow oscillations which can distort event detection. Other temporal and magnitude parameters may be needed. With the necessity to identify many events and compare catchments, we faced the comparison computerized against manual method. Indeed, the will to avoid any manual subjectivity is rapidly curbed by the great diversity of event categories which prevents a repeatable approach. On the other hand, such a process based on type of events must record flow pulses following rainfalls so as events with no flow response. The absence of pulse remains as an important feature to assess. A natural creek like Lyrebird wouldn’t respond with less than 25 to 30 mm of rain because of the great initial losses and stormwater infiltration. So, here also lies the complexity of a unique process dividing events in several categories in accordance with rain depth, rain intensity, rain duration, wetness condition, number of flow pulses or peak flow shapes.
Little Stringybark Creek tributaries. Data issues were encountered for those sites before 2010 and didn’t allow us to continue the analysis. Nonetheless, focusing on the three tributaries could provide insight into the spatial behaviour of the watershed: main baseflow inputs, major runoff contributions, etc. Such an extensive work would also benefit from a better estimation of the rain spatial distribution. Gauges used from Melbourne Water cover indeed vast areas. Thus, using local gauges and Thiessen polygons would enable a better estimation of the actual stormwater volume.
2.6.Conclusion This study attempted to highlight ameliorations in an altered stream following the implementation of new stormwater management systems. We explored different flow metrics which considers several aspects of the flow-regime: magnitude, frequency, duration, rate of change and timing. The analysis mainly focused on features we judged critical: the weak baseflow inputs, the high flashiness of the stream and specific runoffs volumes. While comparing with other natural and urban creeks, the results didn’t show clear meliorative trends. We conclude that the prediction of hydrologic improvements over the 5 years following the retrofit may be premature. Besides, the use of automated processes to identify stormflow events brought out new limitations in the comparison with undeveloped streams. Unlike previous method based on daily, monthly or annual values, it seemed to require new approaches and parameters. It remains an early stage though, and extensive works would encompass more data and larger rain-flow events to specify the true catchments behaviours.
Finally, we would need to complete the flow metrics analysis based on time-series. Further studies would extend the comparison to others catchments like Olinda or Sassafras but also to 16
2.7.Acknowledgements
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