Hydrogeology Journal (2015) 23: 335–350 DOI 10.1007/s10040-014-1200-7
Ground truthing groundwater-recharge estimates derived from remotely sensed evapotranspiration: a case in South Australia Russell S. Crosbie & Phil Davies & Nikki Harrington & Sebastien Lamontagne Abstract Using a water balance to estimate groundwater recharge through the use of remotely sensed evapotranspiration offers a spatial and temporal density of data that other techniques cannot match. However, the estimates are uncertain and therefore ground truthing of the recharge estimates is necessary. This study, conducted in the south-east of South Australia, demonstrated that the raw water-balance estimates of recharge had a negative bias of 45 mm/yr when compared to 190 recharge estimates using the watertable fluctuation method over a 10-year period (2001– 2010). As this bias was not related to the magnitude of the recharge estimated using the water-table fluctuation method, a simple offset was used to bias-correct the water-balance recharge estimates. The bias-corrected recharge estimates had a mean residual that was not significantly different from an independent set of 99 historical recharge estimates but did have a large mean absolute residual indicating a lack of precision. The value in this technique is the density of the data (250m grid over 29,000 km2). The relationship between the water-table depth and net recharge under different vegetation types was investigated. Under pastures, there was no relationship with water-table depth, as the shallow roots do not intercept groundwater. However, under plantation forestry, there was a relationship between net recharge and water-table depth. Net recharge under plantation forestry growing on sandy soils was independent of the water table at around 6 m depth but, under heavier textured soils, the Received: 20 April 2014 / Accepted: 15 October 2014 Published online: 1 November 2014 * Her Majesty the Queen in Right of Australia 2014 R. S. Crosbie ()) : P. Davies : S. Lamontagne CSIRO Land and Water Flagship, PMB 2, Glen Osmond, SA 5064, Australia e-mail:
[email protected] Tel.: +618 8303 8751 N. Harrington National Centre for Groundwater Research and Training, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
trees were using groundwater from depths of more than 20 m. Keywords Australia . Groundwater recharge/water budget . Water balance . Water-table fluctuation . Chloride mass balance
Introduction Groundwater recharge is one of the most difficult elements of the water balance to estimate because it cannot be directly measured. There are a multitude of techniques available for estimating recharge, each with its own strengths and weaknesses (Scanlon et al. 2002). One approach gaining more attention is based upon a water balance using remotely sensed estimates of evapotranspiration (ET). In this approach, recharge is estimated as the difference between rainfall and ET if runoff and changes in soil moisture can be ignored. This method has been used recently in Nebraska, USA (Szilagyi and Jozsa 2013; Szilagyi et al. 2011), Hungary (Szilágyi et al. 2012) and South Africa (Műnch et al. 2013). Brunner et al. (2007) have warned that expectations are often too high when using remotely sensed data in groundwater applications and that ground truthing of data is critical. There are considerable uncertainties in both the rainfall and ET estimates that are magnified when using these data to estimate recharge. Kalma et al. (2008) and Glenn et al. (2011) demonstrated that ET estimates from remote sensing can be 15–30 % in error and spatially interpolated rainfall estimates have been estimated to contain over 10 % error (Jones et al. 2009). These errors have been addressed in different ways ranging from acknowledging a systematic underestimation of ET and so accepting the spatial pattern of recharge whilst scaling the magnitude based on a regression with independent estimates of recharge (Brunner et al. 2004), to acknowledging that the recharge estimates may be overestimated without correction (Műnch et al. 2013; Szilágyi et al. 2012). This study aims to demonstrate that recharge estimates derived from the water balance approach can be biascorrected using an independent method of estimating recharge. The advantage of this approach is that the value of the spatial and temporal density of data available using
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the remote sensing water-balance (WB) method can be increased if there is confidence in the magnitude of the long-term average recharge. If there is confidence in the remotely sensed recharge estimates then the density of data available permits analysis options that have never before been possible.
Study region The study region is part of the Otway and Murray Basins in south-eastern Australia. The extent of the study region is the outline of the tertiary confined sands aquifer of the Dilwyn Formation covering an area of 29,000 km2; this region was chosen for a wider study that is developing a groundwater model for managing the water resources of the region. The Palaeocene Dilwyn Formation was overlain by the unconfined Gambier/Murray Group limestone aquifer of Oligocene and Miocene age (Harrington et al. 2013). The area has a gentle relief with the highest elevation in the north-east with the land sloping away toward the south and west to the coast with some variation due to the dune/flat systems caused by marine transgressions in the Pleistocene (Fig. 1). The climate is water-limited Mediterranean with annual rainfall greatest in the south and decreasing inland (Jones et al. 2009) and potential evapotranspiration (Donohue et al. 2010) greater than rainfall everywhere on an annual basis (Fig. 1). The region has wet winters when potential evapotranspiration is low (Fig. 2) and, thus, has considerable recharge to the groundwater system which is the only source of water in the region. Very little surface runoff is generated, any runoff being quickly infiltrated into the groundwater through thousands of karst features in the landscape (but a minor source of recharge regionally; Leaney and Herczeg 1995). Waterlogging by poor surface-water drainage is alleviated by a complex system of drains. The predominant land use (ABAREBRS 2010) in the region is pasture for grazing (55 % of area), although the plantation forestry (10 % of area) and irrigation industries (3 % of area) are considerable consumers of water. Increasing concern over the management of water resources in the region initiated the development of a quantitative regional groundwater model, of which the present study forms a small part (Harrington and Lamontagne 2013). As well as characterising the hydrogeology of the region, an accurate water balance is necessary to develop a numerical model. The present study is reporting on the recharge side of the water balance. The time scales of interest to this study are initially a long-term average recharge but also an annual series. Ultimately, a monthly time series will be required for the numerical modelling, but this is not the goal of the current paper. The study region has had a long history of field investigations into groundwater recharge from both a water-resources-management point of view (Brown et al. 2006; Wohling 2008; Wood 2010) and a Hydrogeology Journal (2015) 23: 335–350
scientific-methods-development perspective (Allison and Hughes 1978; Anderson 1945). These recharge estimates range from 0 to 375 mm/yr, with an average of 49 mm/yr (a median of 22 mm/yr and a geometric mean of 16 mm/yr). The majority of these recharge estimates are point-based estimates and are then upscaled using a combination of soil type and land use or a physiographic classification of the landscape. Although there are a large number of point recharge estimates that have been made in the past within the study region, not every combination of soil type, land use and climate has been sampled. Recharge estimates derived from remotely sensed evapotranspiration have complete coverage of the study region at a temporal scale that has not been possible with other methods in the past.
Methods The recharge estimates that are currently used for water management in the region are (mostly) derived from the water-table fluctuation (WTF) method (Brown et al. 2006). These recharge estimates have been accepted by the community and formalised in the water allocation plan (SENRMB 2013). The WB estimates of recharge derived from remotely sensed ET provide better spatial and temporal resolution than the existing recharge estimates but have not been evaluated for their accuracy. The chloride mass-balance (CMB) method of estimating recharge is the most widely used method of estimating recharge in Australia (Crosbie et al. 2010) and so has also been used here. The three methods will be compared and the WB estimates bias-corrected if necessary. The three methods of estimating recharge used here are complementary because the spatial and temporal scales of observation are different and there is also a difference in the quantities of water being estimated. The WB method is estimating recharge on a 250-m grid every 8 days, which is the highest spatial and temporal resolution method that is currently available for regional groundwater recharge estimation. The WTF method is a small spatial-scale estimate of recharge. It is an integrated estimate of recharge over an area close to the observation well that is used, and in this highly transmissive aquifer it is probably on the order of several hectares. The WTF method is estimating recharge at an annual scale in the way that it is being implemented here; the annual estimates of recharge can be averaged over the 10 years of WB estimates of recharge for a direct temporal comparison. The CMB method of estimating recharge is estimating recharge from an unknown area upgradient of the point of observation and over a temporal scale that spans the residence time of the water in the aquifer. The WTF method is estimating gross recharge, which is the amount of water that reaches the water table. The CMB and the WB methods are estimating net recharge because they are estimating gross recharge minus ET from the water table. These two net recharge estimates are further differentiated because the CMB cannot estimate a DOI 10.1007/s10040-014-1200-7
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Fig. 1 Background information on the study area showing: elevation, mean annual rainfall (P), mean annual potential evapotranspiration (PET), land use, soil clay content and average depth to water table (DTWT). Inset shows location of study area within Australia (SA South Australia; AHD Australia Height Datum; brown lines are roads)
magnitude below 0 and so cannot be used in groundwater discharge areas, whereas the water balance can estimate a Hydrogeology Journal (2015) 23: 335–350
negative net recharge and therefore can be used in groundwater discharge areas. It would be expected across DOI 10.1007/s10040-014-1200-7
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Fig. 2 Basic climate data for four stations in the study area showing the average monthly rainfall and potential evapotranspiration (PET; Smith et al. 1992) on the top panel and a time series of annual rainfall for the period 2001–2010 on the bottom panel. Data sourced from SILO Patched Point Dataset (Jeffrey et al. 2001)
large spatial scales that the WTF method would give the highest magnitude of recharge followed by the CMB and the WB method should give the lowest estimate of recharge.
Water balance estimates of recharge Using satellite estimates of evapotranspiration (ET) as a means of estimating net recharge (Szilagyi et al. 2011) is a relatively new method. It relies on a water balance (WB) where net recharge (R) can be estimated as the difference between rainfall (P) and ET if runoff and changes in soil moisture storage can be ignored: R ≈ P ET
ð1Þ
Changes in soil moisture are assumed to be negligible on annual or longer timescales and runoff forms a negligible part of the water balance in the study region. The WB method is an estimate of net recharge, which means that the recharge can be either positive or negative. Negative net recharge is indicative of ET being greater than rainfall, which means that there is an additional source of water accessible to the vegetation. Negative net recharge is expected to be prominent in irrigation areas where groundwater extraction provides an additional source of water and in plantation forestry areas where the trees are directly accessing groundwater. The advantage of this method is the spatial and temporal density of Hydrogeology Journal (2015) 23: 335–350
the data; however, the accuracy of the recharge estimates has not been assessed. Estimates of evapotranspiration by remote sensing were derived using the CSIRO MODIS reflectance based scaling evapotranspiration (CMRSET) algorithm (Guerschman et al. 2009). This uses 8-day aggregated MODIS data to produce ET estimates on a 250-mresolution grid. The actual ET estimates are scaled from potential ET using a relationship that uses the enhanced vegetation index (EVI) and the global vegetation moisture index (GVMI). In a comparative study of various ETestimation algorithms against a range of metrics, the CMRSET algorithm was determined to provide the most reliable estimates across Australia (Glenn et al. 2011; King et al. 2011). The rainfall data used in the WB method of estimating recharge was obtained from a Bureau of Meteorology gridded product described by Jones et al. (2009) which features a daily temporal resolution and 0.05° spatial resolution. This data has been oversampled to match the resolution of the CMRSET data.
Water-table fluctuation estimates of recharge
The WTF method of recharge estimation was first proposed by Meinzer and Stearns (1929) and remains well used due to its simplicity (Healy and Cook 2002). The method assumes that water-table rises are caused by recharge. If the specific yield of the unconfined aquifer in question is known, then recharge (R) can be calculated as DOI 10.1007/s10040-014-1200-7
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the change in water level (Δh) multiplied by the specific yield (Sy): R ¼ Δh S y
ð2Þ
Recharge calculated using the WTF method is usually estimated on an event basis. If the water table is shallow and responds quickly to rainfall, the method provides an estimate of gross recharge. If the recharge is calculated over longer time periods (such as annually), then the recharge will be underestimated, since groundwater discharge during the time of measurements is not accounted for. As a method of gross recharge, the WTF method cannot produce negative recharge estimates. There have been some advancements in the methodology of estimating recharge using WTF over the past decade, in particular Crosbie et al. (2005) focused upon the rising limb of the hydrograph and Cuthbert (2010) focused on the falling limb of the hydrograph. However, these improvements have not been used for this study due to the lack of adequate data. Here the approach of Brown et al. (2006) will be used to remain consistent with the current approach used to estimate recharge in the study region. This approach uses seasonal (i.e. quarterly) measurements of groundwater level and a specific yield value of 0.1. This was used on a subset of observation bores that were less than 50 m deep, featured a depth to water table less than 10 m, and featured at least 5 years of data during the period 1970– 2012. The use of a maximum observation of depth to water of 10 m follows on from the analysis of Brown et al. (2006) that showed that at depths greater than this the WTF method gave low results due to attenuation of the recharge signal. There were 464 observation boreholes within the study area that fulfilled these criteria (439 in South Australia and 25 in Victoria). Since a large amount of monitoring data is available for the study area, analysis of the observations required automation. Previous approaches to automate the WTF method have relied on regular high frequency monitoring data (Crosbie et al. 2005). In the present application, a more flexible approach was necessary due to irregular measurement frequencies. To automate the process the change in groundwater level was calculated as the difference between (1) the minimum groundwater level recorded before July 1 of each calendar year and (2) the subsequent maximum groundwater level occurring before the end of the calendar year. In the region, the annual minimum groundwater level generally occurs at the end of the Austral summer (March/April) and the maximum groundwater level at the end of spring (November). These changes in groundwater level were then multiplied by the adopted specific yield to create an annual series of recharge. There is a large amount of uncertainty around the use of a single value of specific yield in the WTF method. Mustafa and Lawson (2002) reviewed all available data from the Gambier Limestone and concluded that all estimates of specific yield had low reliability. There have Hydrogeology Journal (2015) 23: 335–350
been estimates made from short-term pump tests of the specific yield of between 0.075 and 0.3 for the unconfined aquifer; however, these were considered as overestimates because the pumping wells were generally open bores that produced water from the confined and unconfined parts of the aquifer, whilst the observation bores were screened in the unconfined part of the aquifer (Mustafa and Lawson 2002). The value of specific yield adopted here, 0.1 (Brown et al. 2006), is the best estimate currently available but any error in this value propagates directly into the recharge estimates. Equation (2) shows that a 50 % error in specific yield results in a 50 % error in recharge.
Chloride mass-balance estimates of recharge The CMB method of estimating recharge has been used for many years (Anderson 1945) and continues to be used because it is conceptually simple and cheap to implement. The method works because evapotranspiration will remove water from the system and concentrate chloride in the groundwater when compared to the rainfall. Wood (1999) listed some of the assumptions behind the methodology as (1) chloride only originates from rainfall; (2) the chloride is conservative; (3) the mass flux is time invariant; and (4) there is no recycling of chloride. Recharge is calculated as: R¼
D Cg
ð3Þ
where D is the chloride deposition at the land surface and Cg is the chloride concentration of the groundwater. The chloride deposition used for this study was from Leaney et al. (2011), which is a modelled continental surface using 297 field observations and the relationship with distance from the coast of Keywood et al.(1997). The chloride in groundwater observations were collected by the state agencies in South Australia and Victoria over many decades. There were 4,131 bores with chloride in groundwater information (2,791 in South Australia, 1,340 in Victoria).
Bias correction of water balance estimates of recharge The CMRSET data are not locally calibrated and so may have a systematic bias in the study region. In a comparison with water balances derived from streamflow in the South-Eastern Drainage Division (within which the study area is located) there were some catchments where ET was underestimated but more catchments where ET was overestimated (King et al. 2011). If a systematic bias can be identified then the recharge estimates can be corrected to remove the bias. The assumptions behind the recharge estimates from WB, WTF, and CMB mean that the definition of recharge DOI 10.1007/s10040-014-1200-7
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is different in each case, with the WB and CMB being estimates of net recharge and the WTF an estimate of gross recharge. These differences in definition of recharge mean that the quantity of water being compared is not equal; therefore, it confounds the analysis (Crosbie et al. 2010). To get around this problem only a sub-set of the WTF and CMB recharge estimates were compared on a pixel basis to the WB recharge estimates where the definitions of recharge are expected to produce the closest results. The comparison between methods was made for a subset of the data to account for some of the differences between methods. Plantation forestry in the study area is known to use groundwater (Benyon et al. 2006), which can produce a negative net recharge. Similarly, groundwater extraction associated with irrigation areas is included in the net recharge definition of the WB so any WTF or CMB recharge estimates under trees or irrigation areas have been excluded. The capillary rise of groundwater to the surface can occur when the water table is shallow and this can also produce a negative net recharge estimate and so any WTF or CMB recharge estimates over a shallow water table have also been excluded. To enable a temporal examination of trends, any WTF recharge estimates that did not have 6 years data in the period of the WB recharge estimates (2001–2010) were also excluded. These criteria for comparison mean that 190 WTF and 1,701 CMB recharge estimates remained under pasture or cropping land-use types with a depth to water table of greater than 2 m.
Results Water balance estimates of recharge The spatial distribution of average net recharge derived from the WB method using satellite ET estimates over the period 2001 to 2010 (Fig. 3) appears to be consistent with expectations considering the distribution of vegetation types and the climate gradient. The coastal lakes north of Kingston SE (locations are shown on Fig. 1) and areas located between Robe and Beachport feature negative net recharge rates, as would be expected for areas of open water where ET exceeds precipitation in a semi-arid (i.e. water-limited) environment. Irrigation areas (Fig. 1) located in the north of the study area are identifiable as areas of negative net recharge whereas irrigation areas in the south are less prominent, due to relatively higher rates of rainfall. Other visible areas of high negative net recharge include hardwood plantations located to the west of Penola (Fig. 1) and softwood plantations located to the east and south of Penola and to the east of Mount Gambier (Fig. 1). Regions of highest positive net recharge are associated with areas of cropping and pasture located between Kingston, Millicent and Mount Gambier (where rainfall is highest). Locations of limited positive net recharge are distributed throughout the study area and are mainly associated with cropping and pasture land uses. Hydrogeology Journal (2015) 23: 335–350
Using the water balance approach, net recharge over the period 2001 to 2010 and averaged over the entire study area is estimated to be −5 mm (−0.9 % of rainfall), which represents an overall net discharge. When this result is partitioned according to vegetation type, the median net recharge is positive for cropping (i.e. +2.8 % of rainfall) and pasture (+1.4 %) and negative for native vegetation (−3.6 %), softwood forestry (−9.7 %), irrigation (−13.4 %) and hardwood forestry (−16.4 %). When these results are examined on a per-pixel basis (Fig. 4), considerable dispersion around the median is apparent for each vegetation class, and for all classes the range of values includes both positive and negative rates of net recharge.
Water-table fluctuation estimates of recharge Recharge calculated using the WTF method ranged from 2 to 259 mm/yr with an average of 85 mm/yr (a median of 83 mm/yr and a geometric mean of 73 mm/yr; Fig. 3). This average is not representative of the model domain as a whole because of the bias due to the sampling of bores with shallow depth to water table. Significant interannual variability in estimated recharge also exists, with an average coefficient of variation across all sites of 0.53. Two example hydrographs are shown in Fig. 5 with the resultant annual series of recharge. The relationship between vegetation type and recharge estimated using the WTF method is consistent with the results of many reviews of recharge over the years (Crosbie et al. 2010; Kim and Jackson 2012; Petheram et al. 2002; Scanlon et al. 2006; Fig. 4). Irrigated vegetation is associated with the highest recharge due to the extra source of water in addition to precipitation. However, if irrigation water is sourced from groundwater, this will not result in an addition to groundwater storage, since evapotranspiration would be increased above dryland agricultural uses. The next highest recharge estimates are associated with cropping and pasture land use types, whereas the lowest recharge estimates are associated with native vegetation and softwood forestry.
Chloride mass balance estimates of recharge The average of the 4,131 recharge estimates from the CMB is 49 mm/yr (a median of 21 mm/yr and a geometric mean of 19 mm/yr; Fig. 3). This average is not spatially representative of the study area as a whole because of the large number of recharge estimates in the south where the rainfall is highest. The relationship between recharge and vegetation type is not what would be expected with the softwoods having the highest recharge and the crops the lowest (Fig. 4). These results are counter-intuitive because they reflect land use change over time. The CMB estimates of recharge are averaged over the residence time of the water in the aquifer and so do not account for land use change. The land use history of the area is that the native vegetation was largely cleared for agriculture over 100 years ago and then over recent decades DOI 10.1007/s10040-014-1200-7
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Fig. 3 Net recharge as estimated from a water balance using satellite derived ET data compared to a gross recharge estimated using the water-table fluctuation (WTF) method and b net recharge estimated using the chloride mass balance (CMB) method
plantation forestry has been replacing agricultural land in the higher rainfall areas. The recharge estimates under crops are probably an estimate of the native vegetation recharge in the drier areas of the region and the softwoods class is probably an estimate of recharge under mostly pasture in the wetter areas of the region.
Comparison between methods of estimating recharge The spatial patterns of recharge from the WB and WTF methods do not look similar on initial inspection (Fig. 3). This is due to the difference in the recharge definitions and the incomplete spatial coverage of the WTF recharge
estimates. When the recharge estimates are compared by vegetation types, it becomes apparent that the relative trends between methods are the same with the exception of the irrigated land (Fig. 4). The difference under irrigation is due to how recharge is defined for the two methods. Using the WTF method, the irrigated land use has the highest recharge, whereas the WB gives close to the lowest. The high recharge using the WTF method is because the additional water supplied means that the soil is wetter and more water passes through the root zone to become recharge. The WB method does not account for the additional water from irrigation and so sees that the ET is greater than the rainfall and estimates a negative net recharge.
Fig. 4 Boxplots of recharge under different vegetation types as estimated from the a water balance (WB), b water-table fluctuation (WTF) and c chloride mass balance (CMB) methods. The box represents the interquartile range, the line in the box is the median and the lines outside the box represent the 5th and 95th percentiles of the data Hydrogeology Journal (2015) 23: 335–350
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Fig. 5 Example hydrographs from two observation bores (black dots and lines) used to estimate the annual recharge (grey bars): a bore BEN003 at 37.76°S, 140.53°E; b bore HIN005 at 37.66°S, 140.45°E
The other vegetation types are consistent in the relative rates of recharge with the shallow rooted crops and pastures having considerably more recharge than the deep-rooted tree vegetation types, which is consistent with previous studies (Crosbie et al. 2010). The spatial patterns between the WB and CMB methods appear to be closer than between the WB and WTF methods, which is due to the higher recharge in the south from both methods (Fig. 3). When the methods are compared by vegetation type it is clear that the trends are very different, which is due to the CMB method not accounting for the land use change through time and the uncertainty around where the recharge estimates are representative of in space. The WB and WTF methods were compared on a pixel basis, for a subset of the WTF estimates under crops and pastures with deep water tables (>2 m) where the definition of recharge is most consistent. It can be seen that the WTF method gives higher recharge estimates on average, but this is not consistent across all 190 points (Figs. 6 and 7). One possible explanation for the spread in these data points is the accuracy in the spatial data that facilitates the comparison. This ranges from the accuracy in the co-ordinates of the observation bores to the identification of the vegetation type in the land use mapping. The same subset criteria were used to compare the WB and CMB recharge estimates. It can be seen that the CMB method gives higher recharge estimates on average but this is not consistent across all 1,701 points (Figs. 6 and 7). The annual series of recharge was compared for this subset of the data for the WB and WTF methods (the CMB estimates were excluded because they have no time series). This was achieved by averaging the 190 points and then normalising (subtract mean, divide by standard deviation) to allow a comparison without the differences in mean and variance being part of the comparison. It can be seen that the two annual series match quite well (R2 = 0.64) with the greatest difference between the two methods being in 2010 (Fig. 8). The difference in the mean of the two time series is due to the bias between the two methods and the difference in the variance is partially due to the difference in the definition of recharge. The WB Hydrogeology Journal (2015) 23: 335–350
method assumes that there is no change in storage, an assumption which gets increasingly violated with a shorter time step. The annual series of recharge from the WB method has years of negative recharge under pasture due to the change in soil moisture storage that is not accounted for in the method used. For a long-term average, the change in soil moisture storage becomes less of an issue because the error introduced is a smaller proportion of the recharge estimated.
Bias-corrected water-balance recharge estimates The previous section demonstrated that for the WB and CMB methods, the trends in recharge between vegetation types were not consistent and neither was the magnitude of the recharge estimated. The WB and WTF methods of recharge estimation are consistent for trends in recharge between vegetation types as well as the temporal sequencing of the recharge. However, the average recharge when compared at a point scale is different for the two methods for a subset of points where they should be equal, which demonstrates a bias between the two methods. As this bias between the WB and WTF recharge estimates is independent of the magnitude of the recharge estimated from the WTF method (Fig. 7), this would indicate that a single offset value can increase the accuracy of the WB method overall but the accuracy at a single pixel is still uncertain. This single offset value is the average of the difference between the two methods for the 190 points; this is 45 mm/yr (standard error 5 mm/yr). The average difference between the WB and CMB recharge estimates is comparable to the difference between the WB and WTF recharge estimates but will not be considered further in this paper because of the issues with the CMB recharge estimates around land use change. However, this comparison can be made in other hydrogeological systems when the chloride concentration of the groundwater is in equilibrium with the current land use. The bias-corrected average net recharge for the study area is shown in Fig. 9; across the entire study area the average is 40 mm/yr (7.3 % of rainfall). The annual series of bias-corrected recharge shows that the lowest spatially DOI 10.1007/s10040-014-1200-7
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Fig. 6 A comparison of the water balance (WB) estimates of recharge against the chloride mass balance (CMB) estimates of recharge and water-table fluctuation (WTF) estimates of recharge for all points (top panel) and for a subset of points under crops and pastures with water tables deeper than 2 m (bottom panel)
averaged recharge for the 10-year period of investigation was −118 mm in 2006 and the greatest spatially averaged recharge was 171 mm in 2010 (Fig. 10).
Discussion Comparison to previous estimates of recharge The comparison made between the WTF and WB estimates of recharge in Fig. 7 was used to correct the bias in the WB estimates of recharge. An independent test for the accuracy of the bias-corrected WB net recharge
estimates is the wealth of historical field studies that have been conducted within the study area. Field studies of Australian recharge (Crosbie et al. 2010) and discharge (O’Grady et al. 2011) have recently been reviewed, providing all available field data to test the net recharge estimates created here. Of the 216 historical recharge estimates that have been made within the study area, only 94 are suitable for use in this comparison due to the differences in the definition of recharge that is used with different methods, this is the same criteria that was in the comparison between the WB and WTF estimates of recharge where the historical recharge estimates were
Fig. 7 Plot of the residuals as the difference between the a WTF and WB methods of estimating recharge (R) and the b CMB and WB methods of estimating recharge for recharge under crops and pastures where the water table is greater than 2 m deep. The red line is the mean residual Hydrogeology Journal (2015) 23: 335–350
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bias; the 13 mm/yr is not statistically significantly different to 0. The much higher MAR shows that there is a lack of precision in the recharge estimates. This has numerous causes such as the generally point-scale field estimates compared to the areally averaged (∼250 m grid) bias-corrected WB estimates, the separation in time of the field data from the current study being up to 60 years, the differences in the definition of recharge and the possibility of mismatched locations being compared. The highest estimates of recharge amongst the historical field studies are from Allison and Hughes (1978) made around Mount Gambier using the CMB method in the unsaturated zone (Figs. 11 and 12). The bias-corrected WB estimates are consistent with these field estimates being amongst the highest recharge areas within the study area. Two of the field studies have covered large parts of the Fig. 8 Annual series of average normalised recharge under crops study area and, thus, the climatic gradient (Wohling 2008; and pastures from both methods of estimating recharge Wood 2010). Curiously, considering the similar methods included if they were on pastures or crops with a water being used, these two studies show a different relationship table that is deeper than 2 m. Additionally, there are five in the comparison with the bias-corrected WB estimates of field estimates of groundwater discharge within the study net recharge. Sixteen of Wohling’s (2008) 25 recharge area (O’Grady et al. 2011). The location of these field estimates are higher than the bias-corrected WB estimates, estimates of recharge and discharge are shown in Fig. 11. whereas there are only 4 of the 32 recharge estimates from The extremes of the field estimates of net recharge are Wood (2010) that are greater than the bias-corrected WB 283 and −440 mm/yr, whilst the extremes of the bias- estimates. The recharge estimates that have been made in the corrected WB net recharge estimates are 256 and lower rainfall areas of the study area (Kennett-Smith et al. −158 mm/yr for the same grid cells (Fig. 12). Overall, 1994; Osei-Bonsu and Barnett 2008; Walker et al. 1987) the mean difference between the field estimates and the display the greatest relative deviation from the biasbias-corrected WB estimates is 13 mm/yr (standard error corrected WB estimates, which indicates that the WB 8 mm/yr) with a mean absolute residual (MAR) of 50 mm/ method is not appropriate to use in areas where the net yr. The small mean residual indicates that there is little recharge is close to 0. The only field estimates of groundwater discharge within the study area were conducted at a plot scale (∼20 m × ∼20 m) under plantation forestry (Benyon et al. 2006). The field estimates have considerably higher groundwater discharge than the bias-corrected WB estimates (Fig. 12); the reason for this is currently unknown.
Relationship between depth to water table and net recharge
Fig. 9 Bias-corrected net recharge estimates as an average of 2001–2010 Hydrogeology Journal (2015) 23: 335–350
Plot-based WB studies have established that plantation forestry (both softwoods and hardwoods) is a significant user of groundwater in the study area and that this groundwater use has a dependence on the depth to groundwater (Benyon et al. 2006). This observation has been transferred into water management policy with plantations over a water table of less than 6 m deep now needing a water allocation (SENRMB 2013). The data generated from the present study is at a much higher density than previously available and can be used to investigate the relationship between net recharge and depth to groundwater (Fig. 1). The shallow-rooted pasture vegetation shows no relationship with depth to the water table (Fig. 13). The similarly shallow-rooted cropping land use has a different relationship with depth to water table in that there is a DOI 10.1007/s10040-014-1200-7
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Fig. 10 Ten-year annual series of bias-corrected recharge
positive net recharge at shallow depths and then the net recharge becomes independent of the water table beyond a depth of around 5 m. This difference could be due to the land under crops remaining fallow for part of the year which allows for more recharge. Regarding the relationship between net recharge and depth to water table (Fig. 13), the deep-rooted native vegetation has a similar pattern to the cropping land use with an overall lower net recharge rate (closer to 0). Ecological optimality theory would suggest that the native vegetation would evolve to use as much of the water as possible in a water-limited environment (Eagleson 1982). The reason for the higher positive net recharge over a shallow water table is unknown at this stage. Localised runoff and redistribution to neighbouring grid cells or sinkholes (Herczeg et al. 1997) could be one of a number of possible causes which would also include waterlogging inhibiting transpiration (Bell 1999). The relationship between net recharge and depth to water table is different again under the two forestry land Hydrogeology Journal (2015) 23: 335–350
uses (Fig. 13). For both the softwoods and hardwoods, there is a negative net recharge at very shallow depths that gets progressively more negative until it reaches a maximum groundwater discharge at around 5 m depth to water table. With increasing depth to water table, the groundwater discharge becomes less until the net recharge becomes independent of the depth to water table, somewhere in the range of 15–20 m deep. This depth is considerably more than the 6 m used in policy (SENRMB 2013) based on the field studies of Benyon et al. (2006), the 7–8 m found using a similar methodology along the Platte River in Nebraska (Szilagyi et al. 2013) and also deeper than the generalised modelling studies of Shah et al. (2007) and Soylu et al. (2011). Although this is not surprising, considering that trees have been known to root down to 70 m (Canadell et al. 1996). Shah et al. (2007) and Soylu et al. (2011) both demonstrated through modelling that soil texture has an important role in the extinction depth of evapotranspiration from groundwater. Investigation of the net recharge DOI 10.1007/s10040-014-1200-7
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Fig. 11 Location of historical field studies for comparison against the bias-corrected water balance estimates of net recharge
relationship with depth to water table under softwood plantation forestry for a soil texture classification based upon clay content (Fig. 1) further demonstrates the importance of soil types (Fig. 14). For the lightest textured soils (
