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Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity
Journal of Hydrology (2008) 349, 56– 67
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/jhydrol
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity Ross S. Brodie a b
a,*
, Stephen Hostetler b, Emily Slatter
b
Geoscience Australia, GPO Box 378, Canberra ACT 2601, Australia Bureau of Rural Sciences, GPO Box 858, Canberra ACT 2601, Australia
Received 11 May 2007; received in revised form 19 October 2007; accepted 22 October 2007
KEYWORDS Streamflow–rainfall lags; Baseflow; Low-flow frequency; Gaining streams; Water extraction effects
A frequency analysis approach was used to investigate the hydraulic connectivity between streams and aquifers, by comparing daily percentiles of streamflow and rainfall. Three Australian streams were examined – a dominantly gaining stream (Wilsons River, NSW), a dominantly gaining stream modified by significant water extraction (Ovens River, Victoria) and a dominantly losing stream (Mooki River, NSW). For the gaining stream examples, a lag is observed between the seasonal peak in the low-flow percentile curves and the seasonal peak in the daily rainfall percentile curve. Cross-correlation was used to calculate the time-shift that provides the best fit between the streamflow and rainfall percentile curves. There is a good correlation (r2 > 0.8) between the reference rainfall percentile curve and the shifted streamflow percentile curves for gaining streams. The lags evident between the rainfall and streamflow percentile curves represent the processes of first replenishing catchment storages (such as soil moisture and groundwater) and subsequent release to the stream. This is largely a function of catchment hydrogeology as well as climate, notably the magnitude and regularity of rainfall events. Catchment size is not a controlling factor. Analysis of these lags provides insights into the dynamics of groundwater recharge, storage and release. Changes in the lag times over the flow percentiles can reflect changes in the dominant catchment storage contributing to streamflow. For the Wilsons River, the contribution from a groundwater system with longer flow paths increases at lower flow percentiles. This can be critical when protecting minimum streamflows, as near-stream groundwater flow may not be the only determining factor. The impact of water extraction can be recognised in this analysis. For the Ovens River, streamflow deficits relative to the rainfall percentile curve correspond to the summer period of high irrigation demand. Such a deficit was also observed for the Wilsons River when only post-development monitoring was used in the analysis. For the losing stream example of the Mooki River, the relationship between the streamflow and rainfall percentile curves was poor due to the significant number of no-flow days and much greater variability in Summary
* Corresponding author. Tel.: +61 262499042; fax: +61 262499939. E-mail address: [email protected] (R.S. Brodie). 0022-1694/$ - see front matter Crown Copyright ª 2007 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2007.10.056
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity
57
the gauging record. This is because the recharge component of rainfall is not returned as streamflow over the year and a portion of streamflow can become aquifer leakage. Hence, the calculated lags derived from this analysis have no hydrological significance for losing streams. Crown Copyright ª 2007 Published by Elsevier B.V. All rights reserved.
Introduction In the evaluation of stream–aquifer connectivity there is a need to develop simple tools using readily available datasets. A useful starting point is hydrographic analysis as streamflow is commonly monitored in catchments. This analysis is also built on a long history of development and application since the foundation work of Boussinesq (1904), Maillet (1905) and Horton (1933). Baseflow analysis of stream hydrographs tends to focus on baseflow separation or recession analysis techniques. The former is dominated by the application of recursive digital filters to remove the high-frequency quickflow signal. The work of Lyne and Hollick (1979), Chapman (1991), Boughton (1993), Jakeman and Hornberger (1993), Chapman and Maxwell (1996) and Furey and Gupta (2001) are some examples of filter algorithms. In recession analysis, streamflow recession curves are individually or collectively analysed to gain an understanding of discharge processes. Traditionally, a linear storage-outflow model based on the classic exponential decay function was used (Maillet, 1905; Barnes, 1939; Bako and Hunt, 1988). However, alternatives have been developed to address non-linearity and variability in flow recession (Toebs and Strang, 1964; Brutsaert and Nieber, 1977; Griffiths and Clausen, 1997; Wittenberg, 1999). Frequency analysis is another approach to understanding baseflow processes. The most common application is the flow duration curve (FDC) where the relationship between streamflow magnitude and frequency is derived. A wide
Figure 1
range of flow statistics can be used in baseflow studies (Tallaksen, 1995; Smakhtin, 2001). This paper describes the development of a simple frequency analysis technique that combines streamflow with rainfall monitoring.
Catchment descriptions Analysis of streamflow and rainfall data was undertaken in three catchments in southeastern Australia (Fig. 1). As the focus was investigating the groundwater contribution to streams, the methodology was initially developed for a dominantly gaining stream, the Wilsons River in coastal New South Wales. By way of contrast, the method was then trialled for a dominantly gaining stream modified by significant water extraction (Ovens River, Victoria) and a dominantly losing stream (Mooki River, NSW). Table 1 summarises the hydrological characteristics of the case study rivers. The status of the Wilsons River as a dominantly gaining stream is indicated by a high baseflow index (BFI), high Q90/Q50 ratio, relatively shallow recession slopes and a shallow slope for the frequency distribution curve (FDC). The dominantly gaining Ovens River has similar statistics. In contrast, the dominantly losing Mooki River has a large percentage of zero-flow days and steep daily recession curves and FDC slope. Intermittent streams like the Mooki also show a greater variability in streamflow as represented by a high ratio between mean and median flows, as well as high annual coefficient of variation.
Location map of study catchments and stream gauging stations in southeastern Australia.
58 Table 1
R.S. Brodie et al. Hydrological parameters relating to the three Australian case study rivers over a standard period of 1975–2005
Parameter
Description 2
Wilsons
Ovens
Mooki
Catchment area
Area of catchment above the stream gauging station (km )
181
1240
5090
Mean flow
Average daily stream flow (ML/d).
449
1390
358
Baseflow index
Long term ratio of derived baseflow to total flow, applying Lyne and Hollick (1979) filter (a = 0.925) on daily streamflow data. Gaining streams have high BFI.
0.700
0.843
0.464
Mean/median ratio
Ratio of mean flow to median flow for streamflow record. High values indicates large fluctuation in streamflow
2.539
2.196
35.43
Q90/Q50
Ratio of 90% exceedance flow to 50% (median) flow for daily streamflow record. Gaining streams have high ratio due to persistent low flows
0.215
0.174
0
FDC slope
Slope of linear regression of frequency distribution curve between Q80 and Q20. FDC derived from daily streamflow data normalised against average flow over period. High baseflow streams have shallow slope (Growns and Marsh, 2000)
62
32
303
Annual coefficient of variance
Average of ratios of standard deviation divided by mean of daily flows calculated on an annual basis. Describes overall variability of stream flow without considering timing of flows
2.421
1.149
5.044
% Zero-flow
Percentage of streamflow record with zero flows. Intermittent (losing) streams have relatively high number of zero-flow days
0
0
15.8
Median recession ratio
Median of all ratios of daily flow rate to flow rate at previous day. Indicator of steepness of recessions. High baseflow streams should have relatively shallow recession slopes (high recession ratios)
0.95
0.94
0.88
Wilsons River, New South Wales The Wilsons River is located on the north coast of New South Wales, about 700 km north of Sydney. The 181 km2 catchment upstream of the gauging station at the village of Eltham is dominated by plateau and low rolling hills formed by Tertiary Lismore Basalt. The area has a warm temperate climate with annual rainfall exceeding 1800 mm. The catchment was originally part of the largest tract of lowland subtropical rainforest in Australia. The combination of volcanic soils, good rainfall and general frost-free status has fostered intensive agricultural development. Most of the catchment has been cleared for dairying, grazing and horticultural crops such as macadamias, avocadoes, coffee and nurseries. Agricultural development coupled with a growing regional population has led to increased demands on water resources. The streams in the catchment are part of the unregulated surface water management area of the Richmond River. There is an embargo on new surface water licences and existing licences restrict extraction to only when the stream is actually flowing (DLWC, 1999). This is designed to protect stream pools, which are important drought refuges for aquatic ecosystems. A particular issue is that peak irrigation demand coincides with the relatively dry late spring to early
summer period. Regional planning has drafted pumping rules based on low-flow percentiles of the Eltham gauging station (DNR, 2006a). Part of the catchment has been classified as highly stressed due to the risk of over-extraction or contamination (DLWC, 1998) and formalised into a groundwater management area (GWMA 804). As a consequence, a water sharing plan for the Tertiary basalt aquifers has been developed and gazetted (DIPNR, 2004). Groundwater in the remaining part of the catchment is managed under a regional plan. Recharge has been estimated as a proportion of average annual rainfall and the annual volume available for consumptive use based on a proportion of recharge (DNR, 2006b). Fig. 2 shows the relationship between the Wilsons River and the groundwater flow systems in the Tertiary volcanic sequence (Brodie and Green, 2002). The shallow profile of soil and weathered or highly fractured basalt forms a local-scale unconfined aquifer. This discharges as hill-slope springs, valley seepages and baseflow to the first-order plateau streams. Deeper (semi)-confined aquifers occur within the vesicular or highly fractured components of basalt flows and interbedded fluvial deposits. The deeper aquifers discharge at lower levels in the landscape, typically as springs along the plateau escarpment.
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity
Figure 2
59
Schematic cross-section of the hydrogeology of the Alstonville Plateau (Brodie and Green, 2002).
Upper Ovens River, Victoria The Upper Ovens River is an unregulated stream in Victoria, about 200 km northeast of Melbourne. The study catchment is the 1240 km2 above the stream gauging station at the town of Myrtleford. The river occupies a narrow highland alluvial valley, surrounded by the Victorian alpine mountains of Mount Buffalo, Selwyn and Hotham. Average annual rainfall is 1200 mm with winters cold and wet, and summers warm and dry. Alpine woodland and grassland, wet ash forest and dry sclerophyll woodland are largely retained in the steep upper reaches of the catchment. Downstream, the alluvial floodplain widens and has been cleared for agriculture. The uplands consist of eroded Devonian granite or Ordovician metasediments. The alluvial sediments of the valley floor can be 70–80 m thick and are composed of clays, silts and discontinuous sands and gravels (Shugg, 1987). Dredging operations and mining activity has extensively modified the near-stream alluvial landscape. Streams in the catchment are generally perennial, though with much reduced flows in summer. A draft streamflow management plan has been developed to manage the impact of extractions by domestic users, irrigators and urban bulk water entitlements (G-MW, 2003). The plan focuses on water management arrangements for low flows during the high-demand summer months. Over 90% of irrigation licences allow direct stream pumping which generally occurs in the summer–autumn period. The remaining Winter Fill licences allow stream pumping to dam storage during the May–October period. A minimum environmental water provision of 100 ML/d (100,000 m3/day) at Myrtleford is proposed (G-MW, 2003). The alluvial aquifers in the catchment are part of the Murmungee groundwater management area (VDSE, 2006). The alluvial streambed generally consists of very coarse gravels and sands, allowing considerable stream–aquifer connectivity (G-MW, 2003). The Upper Ovens has been identified as a gaining stream and the importance of groundwater discharge in maintaining stream flow has been recognised (Shugg, 1987; Cartwright et al., 2005; Holland et al., 2005). With this in mind, one option being explored is the conversion of surface water entitlements to groundwater entitlements
to reduce the impacts of the high demand during the lowflow summer period (Holland et al., 2005).
Mooki River, New South Wales The Mooki River catchment, upstream of the stream gauge at Breeza, is located in northern New South Wales, south west of Tamworth. The catchment has an area of 5090 km2, is bounded to the south by the Liverpool Ranges and incorporates much of the Liverpool Plains. Elevations vary from 1355 m in the ranges to 280 m at the gauging station. The catchment has a temperate climate with rainfall spread uniformly throughout the year, tending to slightly summer dominant in the south west. The average annual rainfall varies across the catchment from 581 mm to 1114 mm and the average annual evaporation rate from 235 mm to 1372 mm. The majority of the Mooki catchment is used for agriculture, with cropping and pastures on the Liverpool Plains, pastures on the hillslopes and remnant trees on the ranges. Both dryland and irrigated agriculture are practised, with the latter accessing water from both the alluvial aquifers and unregulated supplies from the Mooki River. Basement geology consists of Ordovician to Devonian New England Fold Belt metasediments, Permian volcanics and Permo-Triassic Gunnedah Basin sediments. In the Liverpool plains, these units are overlain by the Upper Namoi alluvium and a cover of highly productive, self mulching clay soils. The alluvium consists of a general fining upward sequence of gravels, sands and clays, with an average thickness of 60–80 m, reaching 160 m in paleochannels (Lavitt and Jankowski, 1998). The Mooki River upstream of Breeza has been identified as being a variably connected gaininglosing reach (Ivkovic et al., 2005). Upland tributaries of the Mooki have been mapped as gaining baseflow from the groundwater system. The high streamflow variability and ephemeral nature of the Mooki River is acknowledged in the existing water sharing plan (DSNR, 2003). Surface water allocation totals 27,449 ML/yr and water extraction (particularly during summer), diversion into on-farm storages and land use changes have modified streamflow patterns (DSNR, 2003).
60
R.S. Brodie et al.
Daily percentile analysis Gaining stream example – Wilsons River at Eltham Daily flow percentiles were derived for about 40 years of record from the Wilsons River stream gauge at Eltham. Each
daily streamflow record was assigned its appropriate day of the year (1–365) and then flow statistics calculated for each daily population of streamflow data. Flow percentiles were generated for each day of the calendar year on a 5% incremental basis from Q5 to Q95, as well as the extreme flows of Q1 and Q99. Leap year days (February 29) were
35 1-D Q99 Shifted 106 Days 1-D Q99 7-Day R10
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8000 6000
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80
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100
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Figure 3 Comparison of various daily streamflow percentiles for Wilsons River @ Eltham with moving 7-day average of daily R10 rainfall percentile for Alstonville station 58131. (a) Q99 flow percentile, (b) Q90 flow percentile, (c) Q50 flow percentile, (d) Q10 flow percentile, (e) Q1 flow percentile.
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity
days (plotted as light grey) then the second feature of the plot becomes evident, namely the similarity between the general shape of rainfall and shifted flow pattern. At median flow conditions (Fig. 3c), the lag between streamflow and rainfall is not as long, with a delay of 50 days used instead of 106 days to shift the streamflow record. This trend of decreasing lag time continues through the high-flow regime, with a shift of only 9 days used for the Q1 flow percentile (Fig. 3e). The corresponding trend of increasing noise in the streamflow data are also apparent in these plots, making it more difficult to interpret relationships between streamflow and rainfall. However, the polynomial trendline for the Q1 data suggests that the lag between this high-flow percentile and rainfall is relatively small. This is expected considering the Q1 percentile is dominated by quickflow processes such as direct runoff and interflow.
Wilsons River at Eltham 150
1.0
135
0.9
120
0.8
105
0.7
90
0.6
75
0.5
60
0.4
Lag
45
0.3
30
0.2
15
0.1
0 0
10
20
30
40
50
60
70
Figure 5 Cross-correlation analysis between Wilsons @ Eltham stream flow percentiles (Qx) and Alstonville rainfall 7R10 percentile, including relationship between lag and flow percentiles (left axis) and relationship between maximum cross correlation (rm) and flow percentiles (right axis).
1-Day R5 7-Day R5 Moving Average 1-Day R10 7-Day R10 Moving Average 1-Day R20 7-Day R20 Moving Average 1-Day R50 1-Day E50
50
Rainfall (mm)
40
30
20
10
Figure 4
Feb
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0.0 90 100
Streamflow Percentile (Qx)
60
0 Jan
80
Maximum Correlation Coefficient (rm)
rm
Lag (Days)
ignored in this analysis to ensure that the statistics related to the same population size for each day of the year. Selected percentile curves across the flow regime (Q99, Q90, Q50, Q10 and Q1) are plotted in Fig. 3. These streamflow percentile curves need to be placed in context with the rainfall distribution throughout the year. Fig. 4 shows daily percentile rainfall curves for the rainfall station 58131 at Alsonville, located near the Eltham gauging site. The upper half of the rainfall record is represented as percentiles R50, R20, R10 and R5. The daily median rainfall (R50) is typically zero, as about 60% of the daily rainfall records are no-rain days. The high rainfall percentiles (R5) are more variable as well as higher in magnitude, being dominated by the larger but sporadic rainfall events. To facilitate comparison with the low-flow percentile curves (Fig. 3), a 7-day moving average was applied to the daily rainfall percentiles. This smoothed the data curves but importantly there is no shift in the timing of fluctuations, so that a smoothed R20 peak corresponds with a smoothed R5 peak. This was not the case when longer timeframes (e.g. 11-day or 15-day) were used to smooth the rainfall percentiles. Also frequency analysis of nearby rainfall stations indicated some variations in the magnitude of the rainfall but practically identical seasonal patterns. This suggests that using rainfall data from a single site, rather than aggregated across the catchment, is a reasonable approach. Hence, for convenience, the 7-day R10 rainfall percentile (7R10) from the Alstonville rainfall station was used as the reference to compare the streamflow percentiles. In Fig. 3, the selected flow percentile curves are plotted individually against the 7R10 rainfall percentile curve. As an example, the Q90 streamflow percentile curve (Fig. 3b) is taken to represent baseflow contributions from natural storages such as the shallow groundwater system. The 7R10 rainfall percentile curve represents the significant rainfall events that generate run-off and also recharge to aquifers. There are several important features to this plot of Fig. 3b. Firstly, the distinct time lag between the seasonal 7R10 rainfall peak (in February–April) and the Q90 streamflow peak (in May–June) is clearly evident. This gives this frequency analysis method its name of Q-Lag (Brodie et al., 2007). When the streamflow data are shifted back by 106
61
Jul
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Sep
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Daily rainfall and evaporation percentiles for Alstonville station 58131.
62
R.S. Brodie et al. quence. The closer rm is to +1 the better the correlation between the two data sequences. Fig. 5 shows the outcome of the cross-correlation analysis for the flow percentile curves calculated for the Wilsons at Eltham gauge and with respect to the Alstonville 7R10 rainfall curve. Fig. 5 confirms the relationships indicated in Fig. 3 where the lag between rainfall and streamflow progressively increases down the low-flow regime, reaching a maxima of 116 days for Q99. In general, the correlation between the shifted flow percentile curves and the 7R10 curve is good with the maximum fit values (rm) typically ranging 0.8–0.9 (Fig. 5). The exceptions are the high flow percentiles of Q5 and Q1 as these data sequences are relatively noisy (refer Fig. 3e), so are difficult to compare against the rainfall sequence. There has been significant development of water resources in the Wilsons River catchment over the last two decades, due to population growth and intensifying of land use to horticulture and other enterprises. This is reflected in groundwater allocation in the plateau basalts rapidly rising since the 1980s (Brodie and Green, 2002). To explore the impact of increased water extraction, daily percentile analysis was undertaken on subsets of the Wilsons River data. The record was split into a ‘‘pre-development’’ period of 1958–1979 and a ‘‘post-development’’ period of 1980– 2005 and separately analysed. The lag distributions for these time periods are distinctly different (Fig. 6). The lags associated with the ‘‘pre-development’’ period are shorter than the comparative lags for the ‘‘post-development’’ period. Fig. 7 compares rainfall and flow percentile curves for these two periods. The two 7R10 rainfall percentile curves are broadly similar, although there are differences such as the position of the rainfall maxima. The peaks in the relevant streamflow percentiles lag these rainfall maxima. The Q90 percentiles in the winter months are also similar for the two periods but the post-development flows are relatively depleted in the remaining months. Daily percentile analysis was also undertaken on the streamflow data from nearby Marom Creek, which also drains the Tertiary basalt sequence (Brodie et al., 2007). Although Marom Creek has a smaller catchment (26 km2) and median flows (26 ML/d) the derived lags are similar to
The time lags used to shift the streamflow percentiles to provide a better match with the rainfall 7R10 percentile curve were calculated using cross-correlation analysis (Davis, 1986). Such an approach was used to define time lags between rainfall and recharge effecting groundwater level (Moon et al., 2004). In this study, the daily 7R10 rainfall record is compared with a series of data sequences produced by incrementally shifting the streamflow percentile record by one day at a time. Each of the 364 shifts in the streamflow record is termed a match position and the cross-correlation (r) is calculated using: r¼
COV1;2 s1 s2
ð1Þ
where COV1,2 is the covariance of the two data sequences (e.g. the daily 7R10 rainfall sequence and a shifted daily Q90 streamflow sequence) and s1 and s2 are the standard deviations of these sequences. The shifted streamflow data sequence with the maximum cross-correlation (rm) has statistically the best fit with the 7R10 reference data se-
150 Wilsons All Data Wilsons <1980 Wilsons >1980 Marom Marom <1980
135 120
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100
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Figure 6 Daily percentile analysis showing relationship between lag and streamflow percentiles for Marom Creek and Wilsons River using different time periods.
Wilsons River at Eltham 30
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Figure 7 Comparison of ‘‘pre-development’’ and ‘‘post-development’’ Q90 flow percentiles and 7R10 rainfall percentiles for Wilsons River @ Eltham.
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity
analysis (Fig. 6). The similarity in lags for ‘‘pre-development’’ Marom Creek and Wilsons River suggests that the underlying processes are controlled more by catchment properties (such as geology, soils and climate) rather than catchment size.
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that of the Wilsons River for the pre-1980 period (Fig. 6). This is because the Marom Creek gauging site is now obsolete and most of the streamflow data was collected prior to 1980. This is confirmed by the results obtained when monitoring data from 1980 onwards was removed from the
63
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Figure 8 Comparison of various daily streamflow percentiles for Ovens River @ Myrtleford with moving 7-day average of daily R10 rainfall percentile for Myrtleford station. (a) Q99 flow percentile. (b) Q90 flow percentile. (c) Q50 flow percentile. (d) Q10 flow percentile. (e) Q1 flow percentile.
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Modified gaining stream – Ovens River at Myrtleford Analysis of daily percentiles was undertaken on streamflow data from the Ovens River gauging station at Myrtleford, and compared with the daily rainfall percentiles for the Myrtleford climate station. Fig. 8 presents the relationship between the reference rainfall percentile curve and various flow percentiles, including the shifted flow record as calculated by cross-correlation. There is a lag between the streamflow and rainfall maxima, particularly evident for the lower flow percentiles. The associated time lags range between 28 and 53 days and have a reasonable correlation (rm = 0.79–0.87) as shown in Fig. 9. When compared with the equivalent plots for the Wilsons River (Fig. 3) the match between the shifted flow percentiles and the rainfall percentiles is visually not as good. When the peak late winter–spring streamflows are scaled to match the rainfall scale, there is a significant streamflow deficit relative to rainfall during the remaining months of the calendar year. Also the time span of peak flows during the late winter–spring period decreases down the flow percentiles, from about 4 months for Q10 (Fig. 8d) to less than 2 months for Q99 (Fig. 8a).
Losing stream – Mooki River at Breeza The streamflow record for the Mooki River at Breeza is characterised by significant periods of no-flow conditions. Recessions following significant rainfall tend to be steep and short-lived in contrast to the persistence of recessions evident in a high-baseflow stream such as the Wilsons River. The recession steepness and large number of no-flow days suggests that stream loss to the shallow alluvial aquifer is a significant process.
Ovens River at Myrtleford 100
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Figure 9 Cross-correlation analysis between Ovens River @ Myrtleford stream flow percentiles (Qx) and Myrtleford rainfall 7R10 percentile, including relationship between lag and flow percentiles (left axis) and relationship between maximum cross correlation (rm) and flow percentiles (right axis).
Fig. 10 presents the results of the analysis of streamflow and rainfall daily percentiles for the Mooki River at Breeza. In the extreme low-flow case of the Q99 percentile, the ephemeral nature of the Mooki is represented by an isolated peak, so any curve-fitting with the rainfall percentiles is meaningless (Fig. 10a). There are also significant no-flow conditions for the Q90 percentile (Fig. 10b) which also limits effective cross correlation. The variability in the higher flow percentiles results in little improvement in matching with rainfall when the flow percentile curve is shifted, with the Q10 percentile being a good example (Fig. 10d). Overall, the no-flow conditions evident in the low flow percentiles, combined with the sporadic high-flow percentiles, limits any meaningful relationship with the rainfall percentile curve. This is reflected in Fig. 11 where the relatively low maximum fits suggest that the calculated time lags from this analysis have no real hydrological meaning.
Discussion The results of the daily percentile analysis can be reconciled with current understanding of the hydrological processes in the three case study catchments. Groundwater discharge from a Tertiary basalt plateau maintains flow in the Wilsons River. The conceptual model for groundwater flow recognises a shallow unconfined aquifer in the basalt regolith and (semi)-confined aquifers deeper in the volcanic sequence (Fig. 2). The shallow regolith aquifer has shorter groundwater flow paths (<5 km) before discharging as springs and seepages, compared with the deeper aquifers. The increasing lag evident for lower flow percentiles for the Wilsons River (Fig. 5) reflects an increasing relative contribution from the deeper aquifers with longer flowpaths. Comparing the chemistry of the plateau groundwaters with that of the Wilsons River provides supporting evidence (Fig. 12). There is a chemical evolution from the shallow regolith groundwaters which has the closest association with rainfall to the mid-level basalt groundwaters, largely due to basalt weathering (Fig. 12a). The very deepest groundwaters plot separately from this trend. In terms of hydrochemistry, the Wilsons River samples plot intermediate between the unconfined regolith and mid-level basalt groundwater end-members (Fig. 12b). This suggests that the river receives proportional contributions from both the shallow and mid-level groundwater sources, and minimal discharge from the deepest groundwater type. Also, the stream samples trend toward the mid-level groundwater population with decreasing flow. This shows that the shallow regolith aquifer dominates the higher flow percentiles (>Q50) but decreases relative to discharge from the mid-level basalt source at lower flows. The increasing lag seen in Fig. 5 is consistent with this trend of greater contributions from a groundwater system with longer residence times and flow paths. The historic trends in lags for the Wilsons River seen in Fig. 6 can also be explained in this context. Water extraction since the 1980s has mostly been from in-stream pumping and groundwater extraction from springs associated with the shallow unconfined basalt regolith aquifer. This represents a relative loss from near-stream storage
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity 10 1-D Q99 Shifted 263 Days 1-D Q99 7-Day R10
8
0.12
6
0.08
4
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2 0
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1-D Q90 Shifted 237 Days 1-D Q90 7-Day R10
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Rainfall (mm/d)
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0.16
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10
1-D Q1 Shifted 7 Days 1-D Q1
8000
8
7-Day R10 Poly. (1-D Q1 Shifted 7 Days)
6000
6
4000
4
2000
2
0 Jan
Rainfall (mm/d)
Streamflow (ML/d)
0.20
65
0
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Figure 10 Comparison of various daily streamflow percentiles for Mooki River @ Breeza with moving 7-day average of daily R10 rainfall percentile for Breeza station. (a) Q99 flow percentile, (b) Q90 flow percentile, (c) Q50 flow percentile, (d) Q10 flow percentile, (e) Q1 flow percentile.
that is associated with quick response times and short groundwater flow paths. This means that the groundwater discharge from mid-level basalt aquifers with longer flowpaths become more dominant, as represented by the general lengthening of lags.
The differences between pre- and post-development flow percentiles for the Wilsons River (Fig. 7) are inferred to reflect the impact of increased water extraction. Flows are similar during the winter months when irrigation water demand is limited. Post-development flows are relatively
66
R.S. Brodie et al. Mooki River at Breeza
120
0.4
90
0.3
60
0.2
30
0.1
0 0
10
20
30
40
50
60
70
80
80 40 20
Mg
Streamflow Percentile (Q x)
Ca
Figure 11 Cross correlation analysis between Mooki River @ Breeza stream flow percentiles (Qx) and rainfall 7R10 percentile for Breeza, including relationship between lag and flow percentiles (left axis) and relationship between maximum cross correlation (rm) and flow percentiles (right axis).
SO4
Wilsons >Q25 Wilsons Q50-Q25 Wilsons Q75-Q50 Wilsons Q90-Q75 Wilsons
80 60 40
Conclusions Comparison of streamflow percentiles against a rainfall reference provides insights into the relationship between the stream and catchment storages. For dynamic baseflow systems like the Wilsons River, much of the recharge component of rainfall returns to the stream as groundwater discharge over the year. This closes the water balance to a certain extent, as reflected in the good correlation between the shifted flow percentile curves and the reference rainfall percentile curve.
Cl
60
0.0 90 100
HCO3 Na
20
Cl
80 60 40 20
Mg
20
40
60
80
depleted in the remaining months when water extraction for irrigation occurs. The general similarity in the rainfall 7R10 percentiles for these two periods also suggests that these flow differences are not related to rainfall differences. The impact of water extraction during the summer lowflow period is also reflected in the apparent streamflow deficit relative to rainfall seen for the Ovens River (Fig. 8). This summer pumping is the water management priority for the catchment (G-MW, 2003). Water extraction from the Ovens River is dominated by demand from irrigation licences between December and March, compared to lower and consistent urban water diversions. Such extraction has decreased the magnitude of summer flows and increased the duration of critical low flow events (NECMA, 2006). The similarity in the shape of the Q90 streamflow percentile curve for the Ovens River (Fig. 8b) with the analogous curve for the post-development Wilsons River (Fig. 7), suggests that this concave-upwards shape is characteristic of streams impacted by seasonal losses (such as in-stream pumping). The lags for the Ovens River are relatively constant across the streamflow regime (Fig. 9). This suggests that a single catchment storage is dominant and this is inferred to be the shallow alluvial aquifer.
60 80
20
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60
0.5
20
rm
150
40
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40
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210
60
240
Maximum Correlation Coefficient (rm)
Lag (Days)
0.9
40
Lag
270
SO4 Regolith Groundwater Shallow Basalt Groundwater Mid-Level Basalt Groundwater 80 Deep Basalt Groundwater 60 Rainfall
20
1.0
80
300
Ca
Figure 12 Durov diagrams for (a) groundwaters from aquifers of varying depth in the Lismore Basalt sequence and (b) Wilsons River @ Eltham samples taken at different flow percentiles.
Departures from this rainfall–streamflow correlation represent losses from the water budget from the perspective of the stream. These losses can be attributed to either (or both) natural processes (such as evapotranspiration or leakage to aquifers) or anthropogenic processes (such as consumptive use or land use change). The Ovens River is a good example where the apparent deficit in streamflow relative to the rainfall curve during the non-winter months is interpreted to be due to water extraction. The ‘post-development’ streamflow percentiles for the Wilsons River show a similar concave upwards shape, also reflecting consumptive use during the non-winter months. In losing systems such as the Mooki River, recharge to groundwater is not returned as streamflow over the year and a portion of streamflow can also be lost as aquifer leakage. This is indicated by the poor relationship between the streamflow percentiles and the rainfall reference. In summary, the correlation coefficient from this analysis can provide guidance on whether the stream is dominantly gaining or losing relative to the groundwater system. For
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity gaining streams, changes in the magnitude of the lags between streamflow percentiles and rainfall can provide insights into the catchment storages that contribute to streamflow.
Acknowledgements This work was undertaken as part of the Managing Connected Water Resources Project, funded by the Australian Government Natural Heritage Trust and the Australian Research Council. Information about this project can be found at http://www.connectedwater.gov.au. The authors are grateful to the reviewers for their useful comments.
References Bako, M.D., Hunt, D.N., 1988. Derivation of baseflow recession constant using computer and numerical analysis. Hydrological Sciences Journal 33 (4), 357–367. Barnes, B.S., 1939. The structure of discharge-recession curves. Transactions of American Geophysical Union 20, 721–725. Boughton, W.C., 1993. A hydrograph-based model for estimating water yield of ungauged catchments. Institute of Engineers Australia National Conference. Publ. 93/14, 317–324. Boussinesq, J., 1904. Recherches theoretique sur l’ecoulement des nappes d’eau infiltrees dans le sol et sur le debit des sources. Journal de Mathematiques Pures et Appliquees 10 (5), 5–78. Brodie, R.S., Green, R., 2002. A hydrogeological assessment of the fractured basalt aquifers of the Alstonville Plateau, NSW. Bureau of Rural Sciences, Canberra ACT. Brodie, R.S., Hostetler, S., Slatter, E., 2007. Q-Lag: a new hydrographic approach to understanding stream–aquifer connectivity. Bureau of Rural Sciences, Canberra ACT. Brutsaert, W., Nieber, J.L., 1977. Regionalized drought flow hydrographs from a mature glaciated plateau. Water Resources Research 13 (3), 637–643. Cartwright, I., Maguire, A., Weaver, T., 2005. Groundwater– surface water interaction in the Ovens River, Victoria, Australia. In: Proceedings of the NZHS-IAH-NZSSS 2005 Conference, 28 Nov – 2 Dec 2005, Auckland, New Zealand. Chapman, T.G., 1991. Comment on evaluation of automated techniques for base flow and recession analyses, by R.J. Nathan and T.A. McMahon. Water Resources Research 27 (7), 1783– 1784. Chapman, T.G., Maxwell, A.I., 1996. Baseflow separation – comparison of numerical methods with tracer experiments. Institute Engineers Australia National Conference. Publ. 96/05, 539–545. Davis, J.C., 1986. Statistics and Data Analysis in Geology, second ed. Wiley, New York, pp. 527–579. DIPNR, 2004. Water sharing plan for the Alstonville Plateau groundwater sources (as amended 1 July 2004). NSW Department of Infrastructure, Planning and Natural Resources, Sydney.. DLWC, 1998. Aquifer risk assessment report. NSW Department of Land and Water Conservation, Sydney. . DLWC, 1999. Richmond river catchment stressed rivers assessment report. NSW Department of Land and Water Conservation, Sydney. DNR, 2006a. Richmond River Unregulated Water Sources – Bangalow Area Water Source (March 2006). NSW Department of
67
Natural Resources, Sydney.. DNR, 2006b. North Coast fractured rocks groundwater source. Report Card. NSW Department of Natural Resources, Sydney. . DSNR, 2003. A guide to the water sharing plan for the Phillips Creek, Mooki River, Quirindi Creek and Warrah Creek Water Sources. NSW Department of Sustainable Natural Resources, Sydney. Furey, P.R., Gupta, V.K., 2001. A physically based filter for separating base flow from streamflow time series. Water Resources Research 37 (11), 2709–2722. G-MW, 2003. Upper Ovens River streamflow management plan. Draft report October 2003. Goulburn-Murray Water. . Griffiths, G.A., Clausen, B., 1997. Streamflow recession in basins with multiple water storages. Journal of Hydrology 190, 60–74. Growns, J., Marsh, N., 2000. Characterisation of flow in regulated and unregulated streams in Eastern Australia. Technical Report 3/2000. CRC for Freshwater Ecology, Canberra ACT. Holland, G.F., Barnett, B.G., Evans, R.S., Dudding, M.W., 2005. Taming the time lag: integrated groundwater–surface water management in the Upper Ovens River, Australia. In: Proceedings of the NZHS-IAH-NZSSS 2005 Conference, 28 Nov – 2 Dec, 2005, Auckland, New Zealand. Horton, R.E., 1933. The role of infiltration in the hydrological cycle. Transactions of the American Geophysical Union 14, 446–460. Ivkovic, K.M., Letcher, R.A., Croke, B.F.W., Evans, W.R., Stauffacher M., 2005. A framework for characterising groundwater and river water interactions: a case study for the Namoi catchment, NSW. In: 29th Hydrology and Water Resources Symposium, Engineers Australia, Canberra. Jakeman, A.J., Hornberger, G.M., 1993. How much complexity is warranted in a rainfall–runoff model? Water Resources Research 29, 2637–2649. Lavitt, N., Jankowski, J., 1998. Groundwater sustainability in the Lower Mooki River catchment: a resource at risk. In: Proceedings IAH International Groundwater Conference, Melbourne 8–13 February 1998. International Association of Hydrogeologists, pp. 63–68. Lyne, V., Hollick, M., 1979. Stochastic time-variable rainfall–runoff modelling. Institute of Engineers Australia National Conference. Publ. 79/10, 89–93. Maillet, E., 1905. Essais d’hydraulique souterraine et fluviale. Librairie Sci. Hermann Paris, 218. Moon, S.K., Woo, N.C., Lee, K.S., 2004. Statistical analysis of hydrographs and water-table fluctuation to estimate groundwater recharge. Journal of Hydrology 292, 198–209. NECMA, accessed 2006. Key points about the Upper Ovens catchment. North East Catchment Management Authority. . Shugg, A., 1987. Hydrogeology of the Upper Ovens Valley. Report 1987/5, Geological Survey of Victoria. Smakhtin, V.Y., 2001. Low flow hydrology: a review. Journal of Hydrology 240, 147–186. Tallaksen, L.M., 1995. A review of baseflow recession analysis. Journal of Hydrology 165, 349–370. Toebs, C., Strang, D.D., 1964. On recession curves 1: recession equations. Journal of Hydrology, New Zealand 3 (2), 2–15. VDSE, accessed 2006. Upper Ovens River fact sheet. Victorian Department of Sustainability and Environment. . Wittenberg, H., 1999. Baseflow recession and recharge as nonlinear storage processes. Hydrological Processes 13, 715–726.
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/jhydrol
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity Ross S. Brodie a b
a,*
, Stephen Hostetler b, Emily Slatter
b
Geoscience Australia, GPO Box 378, Canberra ACT 2601, Australia Bureau of Rural Sciences, GPO Box 858, Canberra ACT 2601, Australia
Received 11 May 2007; received in revised form 19 October 2007; accepted 22 October 2007
KEYWORDS Streamflow–rainfall lags; Baseflow; Low-flow frequency; Gaining streams; Water extraction effects
A frequency analysis approach was used to investigate the hydraulic connectivity between streams and aquifers, by comparing daily percentiles of streamflow and rainfall. Three Australian streams were examined – a dominantly gaining stream (Wilsons River, NSW), a dominantly gaining stream modified by significant water extraction (Ovens River, Victoria) and a dominantly losing stream (Mooki River, NSW). For the gaining stream examples, a lag is observed between the seasonal peak in the low-flow percentile curves and the seasonal peak in the daily rainfall percentile curve. Cross-correlation was used to calculate the time-shift that provides the best fit between the streamflow and rainfall percentile curves. There is a good correlation (r2 > 0.8) between the reference rainfall percentile curve and the shifted streamflow percentile curves for gaining streams. The lags evident between the rainfall and streamflow percentile curves represent the processes of first replenishing catchment storages (such as soil moisture and groundwater) and subsequent release to the stream. This is largely a function of catchment hydrogeology as well as climate, notably the magnitude and regularity of rainfall events. Catchment size is not a controlling factor. Analysis of these lags provides insights into the dynamics of groundwater recharge, storage and release. Changes in the lag times over the flow percentiles can reflect changes in the dominant catchment storage contributing to streamflow. For the Wilsons River, the contribution from a groundwater system with longer flow paths increases at lower flow percentiles. This can be critical when protecting minimum streamflows, as near-stream groundwater flow may not be the only determining factor. The impact of water extraction can be recognised in this analysis. For the Ovens River, streamflow deficits relative to the rainfall percentile curve correspond to the summer period of high irrigation demand. Such a deficit was also observed for the Wilsons River when only post-development monitoring was used in the analysis. For the losing stream example of the Mooki River, the relationship between the streamflow and rainfall percentile curves was poor due to the significant number of no-flow days and much greater variability in Summary
* Corresponding author. Tel.: +61 262499042; fax: +61 262499939. E-mail address: [email protected] (R.S. Brodie). 0022-1694/$ - see front matter Crown Copyright ª 2007 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2007.10.056
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity
57
the gauging record. This is because the recharge component of rainfall is not returned as streamflow over the year and a portion of streamflow can become aquifer leakage. Hence, the calculated lags derived from this analysis have no hydrological significance for losing streams. Crown Copyright ª 2007 Published by Elsevier B.V. All rights reserved.
Introduction In the evaluation of stream–aquifer connectivity there is a need to develop simple tools using readily available datasets. A useful starting point is hydrographic analysis as streamflow is commonly monitored in catchments. This analysis is also built on a long history of development and application since the foundation work of Boussinesq (1904), Maillet (1905) and Horton (1933). Baseflow analysis of stream hydrographs tends to focus on baseflow separation or recession analysis techniques. The former is dominated by the application of recursive digital filters to remove the high-frequency quickflow signal. The work of Lyne and Hollick (1979), Chapman (1991), Boughton (1993), Jakeman and Hornberger (1993), Chapman and Maxwell (1996) and Furey and Gupta (2001) are some examples of filter algorithms. In recession analysis, streamflow recession curves are individually or collectively analysed to gain an understanding of discharge processes. Traditionally, a linear storage-outflow model based on the classic exponential decay function was used (Maillet, 1905; Barnes, 1939; Bako and Hunt, 1988). However, alternatives have been developed to address non-linearity and variability in flow recession (Toebs and Strang, 1964; Brutsaert and Nieber, 1977; Griffiths and Clausen, 1997; Wittenberg, 1999). Frequency analysis is another approach to understanding baseflow processes. The most common application is the flow duration curve (FDC) where the relationship between streamflow magnitude and frequency is derived. A wide
Figure 1
range of flow statistics can be used in baseflow studies (Tallaksen, 1995; Smakhtin, 2001). This paper describes the development of a simple frequency analysis technique that combines streamflow with rainfall monitoring.
Catchment descriptions Analysis of streamflow and rainfall data was undertaken in three catchments in southeastern Australia (Fig. 1). As the focus was investigating the groundwater contribution to streams, the methodology was initially developed for a dominantly gaining stream, the Wilsons River in coastal New South Wales. By way of contrast, the method was then trialled for a dominantly gaining stream modified by significant water extraction (Ovens River, Victoria) and a dominantly losing stream (Mooki River, NSW). Table 1 summarises the hydrological characteristics of the case study rivers. The status of the Wilsons River as a dominantly gaining stream is indicated by a high baseflow index (BFI), high Q90/Q50 ratio, relatively shallow recession slopes and a shallow slope for the frequency distribution curve (FDC). The dominantly gaining Ovens River has similar statistics. In contrast, the dominantly losing Mooki River has a large percentage of zero-flow days and steep daily recession curves and FDC slope. Intermittent streams like the Mooki also show a greater variability in streamflow as represented by a high ratio between mean and median flows, as well as high annual coefficient of variation.
Location map of study catchments and stream gauging stations in southeastern Australia.
58 Table 1
R.S. Brodie et al. Hydrological parameters relating to the three Australian case study rivers over a standard period of 1975–2005
Parameter
Description 2
Wilsons
Ovens
Mooki
Catchment area
Area of catchment above the stream gauging station (km )
181
1240
5090
Mean flow
Average daily stream flow (ML/d).
449
1390
358
Baseflow index
Long term ratio of derived baseflow to total flow, applying Lyne and Hollick (1979) filter (a = 0.925) on daily streamflow data. Gaining streams have high BFI.
0.700
0.843
0.464
Mean/median ratio
Ratio of mean flow to median flow for streamflow record. High values indicates large fluctuation in streamflow
2.539
2.196
35.43
Q90/Q50
Ratio of 90% exceedance flow to 50% (median) flow for daily streamflow record. Gaining streams have high ratio due to persistent low flows
0.215
0.174
0
FDC slope
Slope of linear regression of frequency distribution curve between Q80 and Q20. FDC derived from daily streamflow data normalised against average flow over period. High baseflow streams have shallow slope (Growns and Marsh, 2000)
62
32
303
Annual coefficient of variance
Average of ratios of standard deviation divided by mean of daily flows calculated on an annual basis. Describes overall variability of stream flow without considering timing of flows
2.421
1.149
5.044
% Zero-flow
Percentage of streamflow record with zero flows. Intermittent (losing) streams have relatively high number of zero-flow days
0
0
15.8
Median recession ratio
Median of all ratios of daily flow rate to flow rate at previous day. Indicator of steepness of recessions. High baseflow streams should have relatively shallow recession slopes (high recession ratios)
0.95
0.94
0.88
Wilsons River, New South Wales The Wilsons River is located on the north coast of New South Wales, about 700 km north of Sydney. The 181 km2 catchment upstream of the gauging station at the village of Eltham is dominated by plateau and low rolling hills formed by Tertiary Lismore Basalt. The area has a warm temperate climate with annual rainfall exceeding 1800 mm. The catchment was originally part of the largest tract of lowland subtropical rainforest in Australia. The combination of volcanic soils, good rainfall and general frost-free status has fostered intensive agricultural development. Most of the catchment has been cleared for dairying, grazing and horticultural crops such as macadamias, avocadoes, coffee and nurseries. Agricultural development coupled with a growing regional population has led to increased demands on water resources. The streams in the catchment are part of the unregulated surface water management area of the Richmond River. There is an embargo on new surface water licences and existing licences restrict extraction to only when the stream is actually flowing (DLWC, 1999). This is designed to protect stream pools, which are important drought refuges for aquatic ecosystems. A particular issue is that peak irrigation demand coincides with the relatively dry late spring to early
summer period. Regional planning has drafted pumping rules based on low-flow percentiles of the Eltham gauging station (DNR, 2006a). Part of the catchment has been classified as highly stressed due to the risk of over-extraction or contamination (DLWC, 1998) and formalised into a groundwater management area (GWMA 804). As a consequence, a water sharing plan for the Tertiary basalt aquifers has been developed and gazetted (DIPNR, 2004). Groundwater in the remaining part of the catchment is managed under a regional plan. Recharge has been estimated as a proportion of average annual rainfall and the annual volume available for consumptive use based on a proportion of recharge (DNR, 2006b). Fig. 2 shows the relationship between the Wilsons River and the groundwater flow systems in the Tertiary volcanic sequence (Brodie and Green, 2002). The shallow profile of soil and weathered or highly fractured basalt forms a local-scale unconfined aquifer. This discharges as hill-slope springs, valley seepages and baseflow to the first-order plateau streams. Deeper (semi)-confined aquifers occur within the vesicular or highly fractured components of basalt flows and interbedded fluvial deposits. The deeper aquifers discharge at lower levels in the landscape, typically as springs along the plateau escarpment.
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity
Figure 2
59
Schematic cross-section of the hydrogeology of the Alstonville Plateau (Brodie and Green, 2002).
Upper Ovens River, Victoria The Upper Ovens River is an unregulated stream in Victoria, about 200 km northeast of Melbourne. The study catchment is the 1240 km2 above the stream gauging station at the town of Myrtleford. The river occupies a narrow highland alluvial valley, surrounded by the Victorian alpine mountains of Mount Buffalo, Selwyn and Hotham. Average annual rainfall is 1200 mm with winters cold and wet, and summers warm and dry. Alpine woodland and grassland, wet ash forest and dry sclerophyll woodland are largely retained in the steep upper reaches of the catchment. Downstream, the alluvial floodplain widens and has been cleared for agriculture. The uplands consist of eroded Devonian granite or Ordovician metasediments. The alluvial sediments of the valley floor can be 70–80 m thick and are composed of clays, silts and discontinuous sands and gravels (Shugg, 1987). Dredging operations and mining activity has extensively modified the near-stream alluvial landscape. Streams in the catchment are generally perennial, though with much reduced flows in summer. A draft streamflow management plan has been developed to manage the impact of extractions by domestic users, irrigators and urban bulk water entitlements (G-MW, 2003). The plan focuses on water management arrangements for low flows during the high-demand summer months. Over 90% of irrigation licences allow direct stream pumping which generally occurs in the summer–autumn period. The remaining Winter Fill licences allow stream pumping to dam storage during the May–October period. A minimum environmental water provision of 100 ML/d (100,000 m3/day) at Myrtleford is proposed (G-MW, 2003). The alluvial aquifers in the catchment are part of the Murmungee groundwater management area (VDSE, 2006). The alluvial streambed generally consists of very coarse gravels and sands, allowing considerable stream–aquifer connectivity (G-MW, 2003). The Upper Ovens has been identified as a gaining stream and the importance of groundwater discharge in maintaining stream flow has been recognised (Shugg, 1987; Cartwright et al., 2005; Holland et al., 2005). With this in mind, one option being explored is the conversion of surface water entitlements to groundwater entitlements
to reduce the impacts of the high demand during the lowflow summer period (Holland et al., 2005).
Mooki River, New South Wales The Mooki River catchment, upstream of the stream gauge at Breeza, is located in northern New South Wales, south west of Tamworth. The catchment has an area of 5090 km2, is bounded to the south by the Liverpool Ranges and incorporates much of the Liverpool Plains. Elevations vary from 1355 m in the ranges to 280 m at the gauging station. The catchment has a temperate climate with rainfall spread uniformly throughout the year, tending to slightly summer dominant in the south west. The average annual rainfall varies across the catchment from 581 mm to 1114 mm and the average annual evaporation rate from 235 mm to 1372 mm. The majority of the Mooki catchment is used for agriculture, with cropping and pastures on the Liverpool Plains, pastures on the hillslopes and remnant trees on the ranges. Both dryland and irrigated agriculture are practised, with the latter accessing water from both the alluvial aquifers and unregulated supplies from the Mooki River. Basement geology consists of Ordovician to Devonian New England Fold Belt metasediments, Permian volcanics and Permo-Triassic Gunnedah Basin sediments. In the Liverpool plains, these units are overlain by the Upper Namoi alluvium and a cover of highly productive, self mulching clay soils. The alluvium consists of a general fining upward sequence of gravels, sands and clays, with an average thickness of 60–80 m, reaching 160 m in paleochannels (Lavitt and Jankowski, 1998). The Mooki River upstream of Breeza has been identified as being a variably connected gaininglosing reach (Ivkovic et al., 2005). Upland tributaries of the Mooki have been mapped as gaining baseflow from the groundwater system. The high streamflow variability and ephemeral nature of the Mooki River is acknowledged in the existing water sharing plan (DSNR, 2003). Surface water allocation totals 27,449 ML/yr and water extraction (particularly during summer), diversion into on-farm storages and land use changes have modified streamflow patterns (DSNR, 2003).
60
R.S. Brodie et al.
Daily percentile analysis Gaining stream example – Wilsons River at Eltham Daily flow percentiles were derived for about 40 years of record from the Wilsons River stream gauge at Eltham. Each
daily streamflow record was assigned its appropriate day of the year (1–365) and then flow statistics calculated for each daily population of streamflow data. Flow percentiles were generated for each day of the calendar year on a 5% incremental basis from Q5 to Q95, as well as the extreme flows of Q1 and Q99. Leap year days (February 29) were
35 1-D Q99 Shifted 106 Days 1-D Q99 7-Day R10
30 25
60
20
40
15 10
20
5
0
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
35 1-D Q90 Shifted 106 Days 1-D Q90 7-Day R10
160
25
120
20
80
15 10
40
5 0
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec 35
1-D Q50 Shifted 50 Days 1-D Q50 7-Day R10
500 400
15
200
10
100
5 0
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
3000
35 1-D Q10 Shifted 34 Days 1-D Q10 7-Day R10
2500 2000
30 25 20
1500
15
1000
10
500 0 Jan
25 20
300
0 Jan
30
5 0 Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
10000
35
1-D Q1 Shifted 9 Days 1-D Q1 7-Day R10 Q1 Polynomial
8000 6000
30 25 20 15
4000
10 2000 0 Jan
Rainfall (mm/d)
Streamflow (ML/d)
600
Rainfall (mm/d)
0 Jan
Streamflow (ML/d)
30
5
Rainfall (mm/d)
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Figure 3 Comparison of various daily streamflow percentiles for Wilsons River @ Eltham with moving 7-day average of daily R10 rainfall percentile for Alstonville station 58131. (a) Q99 flow percentile, (b) Q90 flow percentile, (c) Q50 flow percentile, (d) Q10 flow percentile, (e) Q1 flow percentile.
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity
days (plotted as light grey) then the second feature of the plot becomes evident, namely the similarity between the general shape of rainfall and shifted flow pattern. At median flow conditions (Fig. 3c), the lag between streamflow and rainfall is not as long, with a delay of 50 days used instead of 106 days to shift the streamflow record. This trend of decreasing lag time continues through the high-flow regime, with a shift of only 9 days used for the Q1 flow percentile (Fig. 3e). The corresponding trend of increasing noise in the streamflow data are also apparent in these plots, making it more difficult to interpret relationships between streamflow and rainfall. However, the polynomial trendline for the Q1 data suggests that the lag between this high-flow percentile and rainfall is relatively small. This is expected considering the Q1 percentile is dominated by quickflow processes such as direct runoff and interflow.
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Figure 5 Cross-correlation analysis between Wilsons @ Eltham stream flow percentiles (Qx) and Alstonville rainfall 7R10 percentile, including relationship between lag and flow percentiles (left axis) and relationship between maximum cross correlation (rm) and flow percentiles (right axis).
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ignored in this analysis to ensure that the statistics related to the same population size for each day of the year. Selected percentile curves across the flow regime (Q99, Q90, Q50, Q10 and Q1) are plotted in Fig. 3. These streamflow percentile curves need to be placed in context with the rainfall distribution throughout the year. Fig. 4 shows daily percentile rainfall curves for the rainfall station 58131 at Alsonville, located near the Eltham gauging site. The upper half of the rainfall record is represented as percentiles R50, R20, R10 and R5. The daily median rainfall (R50) is typically zero, as about 60% of the daily rainfall records are no-rain days. The high rainfall percentiles (R5) are more variable as well as higher in magnitude, being dominated by the larger but sporadic rainfall events. To facilitate comparison with the low-flow percentile curves (Fig. 3), a 7-day moving average was applied to the daily rainfall percentiles. This smoothed the data curves but importantly there is no shift in the timing of fluctuations, so that a smoothed R20 peak corresponds with a smoothed R5 peak. This was not the case when longer timeframes (e.g. 11-day or 15-day) were used to smooth the rainfall percentiles. Also frequency analysis of nearby rainfall stations indicated some variations in the magnitude of the rainfall but practically identical seasonal patterns. This suggests that using rainfall data from a single site, rather than aggregated across the catchment, is a reasonable approach. Hence, for convenience, the 7-day R10 rainfall percentile (7R10) from the Alstonville rainfall station was used as the reference to compare the streamflow percentiles. In Fig. 3, the selected flow percentile curves are plotted individually against the 7R10 rainfall percentile curve. As an example, the Q90 streamflow percentile curve (Fig. 3b) is taken to represent baseflow contributions from natural storages such as the shallow groundwater system. The 7R10 rainfall percentile curve represents the significant rainfall events that generate run-off and also recharge to aquifers. There are several important features to this plot of Fig. 3b. Firstly, the distinct time lag between the seasonal 7R10 rainfall peak (in February–April) and the Q90 streamflow peak (in May–June) is clearly evident. This gives this frequency analysis method its name of Q-Lag (Brodie et al., 2007). When the streamflow data are shifted back by 106
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Daily rainfall and evaporation percentiles for Alstonville station 58131.
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R.S. Brodie et al. quence. The closer rm is to +1 the better the correlation between the two data sequences. Fig. 5 shows the outcome of the cross-correlation analysis for the flow percentile curves calculated for the Wilsons at Eltham gauge and with respect to the Alstonville 7R10 rainfall curve. Fig. 5 confirms the relationships indicated in Fig. 3 where the lag between rainfall and streamflow progressively increases down the low-flow regime, reaching a maxima of 116 days for Q99. In general, the correlation between the shifted flow percentile curves and the 7R10 curve is good with the maximum fit values (rm) typically ranging 0.8–0.9 (Fig. 5). The exceptions are the high flow percentiles of Q5 and Q1 as these data sequences are relatively noisy (refer Fig. 3e), so are difficult to compare against the rainfall sequence. There has been significant development of water resources in the Wilsons River catchment over the last two decades, due to population growth and intensifying of land use to horticulture and other enterprises. This is reflected in groundwater allocation in the plateau basalts rapidly rising since the 1980s (Brodie and Green, 2002). To explore the impact of increased water extraction, daily percentile analysis was undertaken on subsets of the Wilsons River data. The record was split into a ‘‘pre-development’’ period of 1958–1979 and a ‘‘post-development’’ period of 1980– 2005 and separately analysed. The lag distributions for these time periods are distinctly different (Fig. 6). The lags associated with the ‘‘pre-development’’ period are shorter than the comparative lags for the ‘‘post-development’’ period. Fig. 7 compares rainfall and flow percentile curves for these two periods. The two 7R10 rainfall percentile curves are broadly similar, although there are differences such as the position of the rainfall maxima. The peaks in the relevant streamflow percentiles lag these rainfall maxima. The Q90 percentiles in the winter months are also similar for the two periods but the post-development flows are relatively depleted in the remaining months. Daily percentile analysis was also undertaken on the streamflow data from nearby Marom Creek, which also drains the Tertiary basalt sequence (Brodie et al., 2007). Although Marom Creek has a smaller catchment (26 km2) and median flows (26 ML/d) the derived lags are similar to
The time lags used to shift the streamflow percentiles to provide a better match with the rainfall 7R10 percentile curve were calculated using cross-correlation analysis (Davis, 1986). Such an approach was used to define time lags between rainfall and recharge effecting groundwater level (Moon et al., 2004). In this study, the daily 7R10 rainfall record is compared with a series of data sequences produced by incrementally shifting the streamflow percentile record by one day at a time. Each of the 364 shifts in the streamflow record is termed a match position and the cross-correlation (r) is calculated using: r¼
COV1;2 s1 s2
ð1Þ
where COV1,2 is the covariance of the two data sequences (e.g. the daily 7R10 rainfall sequence and a shifted daily Q90 streamflow sequence) and s1 and s2 are the standard deviations of these sequences. The shifted streamflow data sequence with the maximum cross-correlation (rm) has statistically the best fit with the 7R10 reference data se-
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Figure 6 Daily percentile analysis showing relationship between lag and streamflow percentiles for Marom Creek and Wilsons River using different time periods.
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Figure 7 Comparison of ‘‘pre-development’’ and ‘‘post-development’’ Q90 flow percentiles and 7R10 rainfall percentiles for Wilsons River @ Eltham.
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity
analysis (Fig. 6). The similarity in lags for ‘‘pre-development’’ Marom Creek and Wilsons River suggests that the underlying processes are controlled more by catchment properties (such as geology, soils and climate) rather than catchment size.
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that of the Wilsons River for the pre-1980 period (Fig. 6). This is because the Marom Creek gauging site is now obsolete and most of the streamflow data was collected prior to 1980. This is confirmed by the results obtained when monitoring data from 1980 onwards was removed from the
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Figure 8 Comparison of various daily streamflow percentiles for Ovens River @ Myrtleford with moving 7-day average of daily R10 rainfall percentile for Myrtleford station. (a) Q99 flow percentile. (b) Q90 flow percentile. (c) Q50 flow percentile. (d) Q10 flow percentile. (e) Q1 flow percentile.
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Modified gaining stream – Ovens River at Myrtleford Analysis of daily percentiles was undertaken on streamflow data from the Ovens River gauging station at Myrtleford, and compared with the daily rainfall percentiles for the Myrtleford climate station. Fig. 8 presents the relationship between the reference rainfall percentile curve and various flow percentiles, including the shifted flow record as calculated by cross-correlation. There is a lag between the streamflow and rainfall maxima, particularly evident for the lower flow percentiles. The associated time lags range between 28 and 53 days and have a reasonable correlation (rm = 0.79–0.87) as shown in Fig. 9. When compared with the equivalent plots for the Wilsons River (Fig. 3) the match between the shifted flow percentiles and the rainfall percentiles is visually not as good. When the peak late winter–spring streamflows are scaled to match the rainfall scale, there is a significant streamflow deficit relative to rainfall during the remaining months of the calendar year. Also the time span of peak flows during the late winter–spring period decreases down the flow percentiles, from about 4 months for Q10 (Fig. 8d) to less than 2 months for Q99 (Fig. 8a).
Losing stream – Mooki River at Breeza The streamflow record for the Mooki River at Breeza is characterised by significant periods of no-flow conditions. Recessions following significant rainfall tend to be steep and short-lived in contrast to the persistence of recessions evident in a high-baseflow stream such as the Wilsons River. The recession steepness and large number of no-flow days suggests that stream loss to the shallow alluvial aquifer is a significant process.
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Figure 9 Cross-correlation analysis between Ovens River @ Myrtleford stream flow percentiles (Qx) and Myrtleford rainfall 7R10 percentile, including relationship between lag and flow percentiles (left axis) and relationship between maximum cross correlation (rm) and flow percentiles (right axis).
Fig. 10 presents the results of the analysis of streamflow and rainfall daily percentiles for the Mooki River at Breeza. In the extreme low-flow case of the Q99 percentile, the ephemeral nature of the Mooki is represented by an isolated peak, so any curve-fitting with the rainfall percentiles is meaningless (Fig. 10a). There are also significant no-flow conditions for the Q90 percentile (Fig. 10b) which also limits effective cross correlation. The variability in the higher flow percentiles results in little improvement in matching with rainfall when the flow percentile curve is shifted, with the Q10 percentile being a good example (Fig. 10d). Overall, the no-flow conditions evident in the low flow percentiles, combined with the sporadic high-flow percentiles, limits any meaningful relationship with the rainfall percentile curve. This is reflected in Fig. 11 where the relatively low maximum fits suggest that the calculated time lags from this analysis have no real hydrological meaning.
Discussion The results of the daily percentile analysis can be reconciled with current understanding of the hydrological processes in the three case study catchments. Groundwater discharge from a Tertiary basalt plateau maintains flow in the Wilsons River. The conceptual model for groundwater flow recognises a shallow unconfined aquifer in the basalt regolith and (semi)-confined aquifers deeper in the volcanic sequence (Fig. 2). The shallow regolith aquifer has shorter groundwater flow paths (<5 km) before discharging as springs and seepages, compared with the deeper aquifers. The increasing lag evident for lower flow percentiles for the Wilsons River (Fig. 5) reflects an increasing relative contribution from the deeper aquifers with longer flowpaths. Comparing the chemistry of the plateau groundwaters with that of the Wilsons River provides supporting evidence (Fig. 12). There is a chemical evolution from the shallow regolith groundwaters which has the closest association with rainfall to the mid-level basalt groundwaters, largely due to basalt weathering (Fig. 12a). The very deepest groundwaters plot separately from this trend. In terms of hydrochemistry, the Wilsons River samples plot intermediate between the unconfined regolith and mid-level basalt groundwater end-members (Fig. 12b). This suggests that the river receives proportional contributions from both the shallow and mid-level groundwater sources, and minimal discharge from the deepest groundwater type. Also, the stream samples trend toward the mid-level groundwater population with decreasing flow. This shows that the shallow regolith aquifer dominates the higher flow percentiles (>Q50) but decreases relative to discharge from the mid-level basalt source at lower flows. The increasing lag seen in Fig. 5 is consistent with this trend of greater contributions from a groundwater system with longer residence times and flow paths. The historic trends in lags for the Wilsons River seen in Fig. 6 can also be explained in this context. Water extraction since the 1980s has mostly been from in-stream pumping and groundwater extraction from springs associated with the shallow unconfined basalt regolith aquifer. This represents a relative loss from near-stream storage
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity 10 1-D Q99 Shifted 263 Days 1-D Q99 7-Day R10
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Figure 10 Comparison of various daily streamflow percentiles for Mooki River @ Breeza with moving 7-day average of daily R10 rainfall percentile for Breeza station. (a) Q99 flow percentile, (b) Q90 flow percentile, (c) Q50 flow percentile, (d) Q10 flow percentile, (e) Q1 flow percentile.
that is associated with quick response times and short groundwater flow paths. This means that the groundwater discharge from mid-level basalt aquifers with longer flowpaths become more dominant, as represented by the general lengthening of lags.
The differences between pre- and post-development flow percentiles for the Wilsons River (Fig. 7) are inferred to reflect the impact of increased water extraction. Flows are similar during the winter months when irrigation water demand is limited. Post-development flows are relatively
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Figure 11 Cross correlation analysis between Mooki River @ Breeza stream flow percentiles (Qx) and rainfall 7R10 percentile for Breeza, including relationship between lag and flow percentiles (left axis) and relationship between maximum cross correlation (rm) and flow percentiles (right axis).
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Conclusions Comparison of streamflow percentiles against a rainfall reference provides insights into the relationship between the stream and catchment storages. For dynamic baseflow systems like the Wilsons River, much of the recharge component of rainfall returns to the stream as groundwater discharge over the year. This closes the water balance to a certain extent, as reflected in the good correlation between the shifted flow percentile curves and the reference rainfall percentile curve.
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depleted in the remaining months when water extraction for irrigation occurs. The general similarity in the rainfall 7R10 percentiles for these two periods also suggests that these flow differences are not related to rainfall differences. The impact of water extraction during the summer lowflow period is also reflected in the apparent streamflow deficit relative to rainfall seen for the Ovens River (Fig. 8). This summer pumping is the water management priority for the catchment (G-MW, 2003). Water extraction from the Ovens River is dominated by demand from irrigation licences between December and March, compared to lower and consistent urban water diversions. Such extraction has decreased the magnitude of summer flows and increased the duration of critical low flow events (NECMA, 2006). The similarity in the shape of the Q90 streamflow percentile curve for the Ovens River (Fig. 8b) with the analogous curve for the post-development Wilsons River (Fig. 7), suggests that this concave-upwards shape is characteristic of streams impacted by seasonal losses (such as in-stream pumping). The lags for the Ovens River are relatively constant across the streamflow regime (Fig. 9). This suggests that a single catchment storage is dominant and this is inferred to be the shallow alluvial aquifer.
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Figure 12 Durov diagrams for (a) groundwaters from aquifers of varying depth in the Lismore Basalt sequence and (b) Wilsons River @ Eltham samples taken at different flow percentiles.
Departures from this rainfall–streamflow correlation represent losses from the water budget from the perspective of the stream. These losses can be attributed to either (or both) natural processes (such as evapotranspiration or leakage to aquifers) or anthropogenic processes (such as consumptive use or land use change). The Ovens River is a good example where the apparent deficit in streamflow relative to the rainfall curve during the non-winter months is interpreted to be due to water extraction. The ‘post-development’ streamflow percentiles for the Wilsons River show a similar concave upwards shape, also reflecting consumptive use during the non-winter months. In losing systems such as the Mooki River, recharge to groundwater is not returned as streamflow over the year and a portion of streamflow can also be lost as aquifer leakage. This is indicated by the poor relationship between the streamflow percentiles and the rainfall reference. In summary, the correlation coefficient from this analysis can provide guidance on whether the stream is dominantly gaining or losing relative to the groundwater system. For
Comparison of daily percentiles of streamflow and rainfall to investigate stream–aquifer connectivity gaining streams, changes in the magnitude of the lags between streamflow percentiles and rainfall can provide insights into the catchment storages that contribute to streamflow.
Acknowledgements This work was undertaken as part of the Managing Connected Water Resources Project, funded by the Australian Government Natural Heritage Trust and the Australian Research Council. Information about this project can be found at http://www.connectedwater.gov.au. The authors are grateful to the reviewers for their useful comments.
References Bako, M.D., Hunt, D.N., 1988. Derivation of baseflow recession constant using computer and numerical analysis. Hydrological Sciences Journal 33 (4), 357–367. Barnes, B.S., 1939. The structure of discharge-recession curves. Transactions of American Geophysical Union 20, 721–725. Boughton, W.C., 1993. A hydrograph-based model for estimating water yield of ungauged catchments. Institute of Engineers Australia National Conference. Publ. 93/14, 317–324. Boussinesq, J., 1904. Recherches theoretique sur l’ecoulement des nappes d’eau infiltrees dans le sol et sur le debit des sources. Journal de Mathematiques Pures et Appliquees 10 (5), 5–78. Brodie, R.S., Green, R., 2002. A hydrogeological assessment of the fractured basalt aquifers of the Alstonville Plateau, NSW. Bureau of Rural Sciences, Canberra ACT. Brodie, R.S., Hostetler, S., Slatter, E., 2007. Q-Lag: a new hydrographic approach to understanding stream–aquifer connectivity. Bureau of Rural Sciences, Canberra ACT. Brutsaert, W., Nieber, J.L., 1977. Regionalized drought flow hydrographs from a mature glaciated plateau. Water Resources Research 13 (3), 637–643. Cartwright, I., Maguire, A., Weaver, T., 2005. Groundwater– surface water interaction in the Ovens River, Victoria, Australia. In: Proceedings of the NZHS-IAH-NZSSS 2005 Conference, 28 Nov – 2 Dec 2005, Auckland, New Zealand. Chapman, T.G., 1991. Comment on evaluation of automated techniques for base flow and recession analyses, by R.J. Nathan and T.A. McMahon. Water Resources Research 27 (7), 1783– 1784. Chapman, T.G., Maxwell, A.I., 1996. Baseflow separation – comparison of numerical methods with tracer experiments. Institute Engineers Australia National Conference. Publ. 96/05, 539–545. Davis, J.C., 1986. Statistics and Data Analysis in Geology, second ed. Wiley, New York, pp. 527–579. DIPNR, 2004. Water sharing plan for the Alstonville Plateau groundwater sources (as amended 1 July 2004). NSW Department of Infrastructure, Planning and Natural Resources, Sydney.
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Natural Resources, Sydney.