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SIMULATED IMPACTS OF CLIMATE CHANGE ON GROUNDWATER RECHARGE IN THE SUB TROPICS OF QUEENSLAND, AUSTRALIA

Timothy R. Green!, Bryson C. Bates!, P. Mick Fleming2, and Stephen P. Charles! !CSIRO Land and Water, Floreat, Western Australia 6014, Australia 2CSIRO Land and Water, Canberra, ACT 2601, Australia

ABSTRACT Increased atmospheric concentrations of CO2 could affect Australia's groundwater resources via changes in rainfall and potential evapotranspiration regimes. The extent to which groundwater resources are affected by climate change will depend upon the local soils and vegetation. As a case study, we assess the potential impacts of climate change on groundwater recharge beneath North Stradbroke Island off the subtropical east coast of Queensland, Australia. The simulated climates come from equilibrium (constant CO2 concentration) runs of the CSIR09 general circulation model (GCM) for present and double-C02 conditions. Based on the GCM output for each climate, a stochastic point weather generator, MWGEN, produces realisations of the daily climate variables. This climate "data" drives a numerical simulator, WAYES, of rainfall infiltration, variably saturated flow and evapotranspiration, producing temporal distributions of the daily groundwater recharge rate for various soil-vegetation environments. The transformation from rainfall infiltration to groundwater recharge can amplify the effects of climate change because of flow and storage in soils and dynamic plant water use. The simulation results indicate that double-C0 2 climate change could more than double the net groundwater recharge; this increase is disproportionate to a 37 percent rise in mean annual rainfall, with ratios of the change in recharge to change in rainfall ranging from 0.76 to 1.05 for different soil-vegetation combinations. Such increases in recharge are enhanced by the dynamic growth and die-back of vegetation. The mean recharge rate, inter-annual variability and persistence in deviations from the mean are related to the soil and vegetation characteristics. Further improvements in estimating future climate and plant-water use should increase our understanding of the sensitivity of groundwater resources to expected climate change and climate variability.

M. Taniguchi, Subsurface Hydrological Responses to Land Cover and Land Use Changes © Kluwer Academic Publishers 1997

188

SIMULATED IMPACTS OF CLIMATE CHANGE

INTRODUCTION Groundwater is a valuable resource in areas like North Stradbroke Island, Queensland, Australia. Infiltration rates through the sandy soils can be high, such that surface runoff is a relatively minor part of the hydrologic budget, and most water used by humans is pumped from aquifers. Furthermore, groundwater discharge to wetlands on the borders of the island and to inland lakes affects the health of ecosystems in such environments. The hydrologic fluxes of interest may change significantly under different climate regimes. These quantities include temporal variability in groundwater recharge rates and the long-term yield (both human-induced and natural extractions) of the water-table aquifer. In this light, we investigate the potential effects of climate change on the aquifer recharge. Aquifer storage is not considered, and it may be ignored for the purpose of estimating long-term groundwater discharge at dynamic equilibrium. The approach taken here is to let water levels remain at existing elevations and compute the changes in net recharge; recharge and discharge are equivalent for fixed aquifer storage or over long time periods. Plant water use (transpiration) is an important component of the water budget needed to compute groundwater recharge. Transpiration rates are dynamically related to not only atmospheric forcing and soil water availability, but also to the state of the vegetation. As noted by Dooge (1992), "... meaningful scenarios of hydrologic prediction and climate prediction are not possible without an understanding of vegetation response." Some infiltration models have incorporated transpiration modules where plant root densities and leaf areas were prespecified as either static or changing in a consistent seasonal manner. By contrast, the present simulations include effects of dynamic plant growth on transpiration and deep drainage.

BACKGROUND General Circulation Model Global climate change caused by rising atmospheric concentrations of carbon dioxide (C02) may have a significant impact on regional water resources. Recent research suggests that plausible climatic changes will affect the timing and magnitude of runoff and soil moisture, evapotranspiration, and groundwater recharge (McCabe and Ayers, 1989; Lettenmaier and Gan 1990; Cohen, 1991; Mimikou et al. 1991; Vaccaro, 1992; Wilkinson and Cooper, 1993; Bates et aI., 1994; Kirshen and Fennessey, 1995). Climate change scenarios are based on the climatic simulations of numerical models of the general circulation of the atmosphere. General Circulation Models (GCMs) perform reasonably well in simulating the present climate with respect to

GREEN ET AL.

189

annual or seasonal averages at large spatial scales (> 104 km2) but poorly at the smaller time and space scales relevant to hydrological studies. Although GCMs are unanimous in their projections that a doubling of current atmospheric concentrations of CO 2 wi11lead to an increase in the global mean temperature and precipitation (Kattenberg et al., 1996), there are differences in their projected changes in temperature and precipitation at regional scales that are of the same order or larger than the projected global changes (Grotch and MacCracken, 1991; Robock et al., 1993; Gates et aI., 1996). GCM simulations are usually of short duration (about 30 years) and may not capture extreme events, leading to overestimation of daily precipitation frequency, and under-estimation of precipitation amounts. The spectral GCM used in this study (CSIR09 Mark 1) has been developed by the CSIRO Division of Atmospheric Research (McGregor et al., 1993). The model operates with nine vertical levels in the atmosphere and a horizontal resolution of about 300 km by 600 km. The simulated climate data come from 28-year equilibrium runs for the control climate (constant CO 2 concentration of 330 ppm) and future (doubled CO 2) climate. The runs provide daily values for 30 climatic variables including: precipitation; maximum and minimum screen temperature; temperature at level I; and global solar radiation at ground level. These GCM output variables provide information on possible modifications of the frequency and distribution of rainfall events and potential evapotranspiration.

Stochastic Point Weather Generator As a precursor to the present impacts study, we developed a procedure for generating long sequences of daily weather variables for present and double-C0 2 climates. The procedure considers: (l) changes in the distribution and frequency of precipitation events, (2) changes in the form and magnitude of variability of temperature and global solar radiation series, and (3) effects of interannual climate variability as well as long-term climate changes. Furthermore, it preserves the internal consistency of GCM simulations of future climate scenarios by preserving the cross-correlation structure between climate variables (Bates et al., 1994). The stochastic weather generator used in this study is based on the WGEN model (Richardson and Wright, 1984). WGEN uses single harmonics to describe the annual cycles of the means and standard deviations of daily minimum and maximum temperature and global solar radiation. This approach provided poor fits to historical Australian data, particularly in the tropics and subtropics. The modified model, MWGEN, uses higher-order harmonics for temperature and an upper envelope for radiation, considering geographical location and average clear sky conditions. For each calendar month, a generalised beta distribution is fitted to the daily difference between the theoretical maximum global solar radiation and recorded data for wet and dry days. The model is used to generate long-term sequences of synthetic daily weather

190


records which in tum are used to drive a model of the environmental system of interest. The parameters of the stochastic weather model characterise the behavior of the present-day climate. Changed climate sequences can be produced by adjusting some or all· of the model parameters in a manner consistent with GCM trends (e.g., Wilks, 1992; Bates et al., 1994; Tung and Haith, 1995). It assures that assessments of the impact of climate change on hydrological extremes are not confounded by the effects of natural interannual variability (Lettenmaier and Gan, 1990; Lettenmaier and Sheer, 1991).

Study Site Here, we apply results from the above procedure using daily weather sequences for North Stradbroke Island ("Stradbroke") to a soil-atmosphere-vegetation model. Stradbroke is located about 40 km offshore of Brisbane in the eastern state of Queensland, Australia. It stretches about 32 km from north to south with an average latitude of approximately 27.5 degrees south. The main industries on the 285 km2 island are mineral sands mining and tourism. Mining is currently the major industrial use of groundwater, but further pumping of groundwater for urban use on the mainland has been considered (Water Resources Commission, 1991) with subsequent approval to withdraw 15 ML per day (J. Arunakumaren, written comm., 1992). The island is a massive sand dune rising to 229 meters above sea level over weathered rock (primarily Cenozoic deposits, with lesser amounts of Mesozoic sandstone, shale and tuff, and Paleozoic greenstone) to depths of over 90 m below sea level on the eastern side of the island. The dunes consist of fine to medium quartz grains. Deposits of silt-size particles and organic material form the base of inland lakes and swamps (Laycock, 1975). Depths to the main water-table aquifer exceed 50 m over most of the inland areas where natural recharge occurs. Only relatively deep water table conditions (>25 m) are considered in this paper. The sand dunes are covered primarily by mixed forests, dominated by eucalyptus trees. The most common species are Blue Gum (Eucalyptus tereticornis) and scribbly gum (E. signata). Dunes in sand mining areas are stabilised by revegetating with perrenial grasses.

METHODS

A chain of simulation models is used in this study: 1. CSIR09 (Mark 1) General Circulation Model We first selected a CSIR09 GCM grid cell in the vicinity of Stradbroke that best matched the statistics of the historic record. Output from this cell as described above provided daily weather variables for current (Climate 1) and double-C02 (Climate 2) climate simulations.

191

GREEN ET AL.

2. MWGEN (Modified WGEN) stochastic daily point weather generator Two climate scenarios (1000 years each of daily data) for Stradbroke were generated using MWGEN (see above). The climatic variables simulated are precipitation occurrence and amount, maximum and minimum temperature, and global solar radiation for Climates 1 and 2. 3. WAVES soil-vegetation-atmosphere physically based numerical model The WAVES (Water, Vegetation, Energy, and Solutes) model (Dawes and Short, 1993; Zhang et al., 1996) is used to compare recharge values simulated with both static and dynamic vegetation. Version 3.0 of WAVES uses a canopy-scale representation of the vegetation that appears to be robust under the various climatic conditions simulated here. The canopy-scale growth and decay are less dynamic (Le., more conservative) than the leaf-scale dynamics simulated in previous versions (not shown here). We also modified WAVES to include inter-day interception storage, user control of output, and corrected the use of vapor pressure in the energy budget as identified by R. Silberstein (pers. comm., 1996). WAVES includes a finite difference numerical solver for the mixed form (mass conserving) of Richards' equation in one dimension (vertical). The soil-water module uses the Broadbridge and White (1988) functions for soil hydraulic properties, and is very efficient and numerically stable. The soils drain by gravity at the base of the simulated columns (5 m for grasses and 25 m for trees). The computed "deep drainage" is equated with groundwater recharge. Model Parameters Input to the WAVES model includes soil hydraulic properties, vegetation parameter sets, and climatic variables for the two climate scenarios. Soil parameters for five generic soil types (Table 1) cover the range of expected soils for relatively deep unsaturated soil profiles on Stradbroke. High dunes covering most of the island may be characterised as fine to medium sand, but finer textured loams are included in the analyses. Table 1. Broadbridge and White soil hydraulic parameters for five soil types used (Reference numbers are cited in Figures 5, 7 and 8). 9s 9r Soil Type Xc (m) No. C Ks (mJd) 1

Medium Sand

10.00

0.35

0.05

0.025

1.02

2

Fine Sand

1.00

0.35

0.08

0.05

1.02

3

Loam

0.20

0.45

0.10

0.15

1.40

4

Sandy Loam

0.20

0.40

0.07

0.10

1.15

5

Clay Loam

0.10

0.50

0.20

0.30

1.40

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The plant-growth model uses parameters representative of eucalyptus trees and perennial grasses tested under Australian conditions. Limited calibration periods, typically two or three years, have been used to determine the sets of 23 vegetation parameters, while simulations here are run for long time series assuming dynamic equilibrium in climate over decades to centuries. Table 2 shows vegetation parameter sets for four vegetation types: two C4 perennial grasses (modified from the "WAYES Tutorial" by W. R. Dawes, written comm., 1995) and two types of eucalyptus ("gum") trees. Both sets of gum tree parameters were modified to approximate the dominant tree type (Blue Gum) in this humid, subtropical environment. "Grass I" and "Gum I" have a balance of relatively high carbon assimilation rates (Ama0 and high respiration (Ro(L,S,R)) and leaf mortality coefficients (md. "Gum I" is based in part on assimilation and respiration rates for Mountain Ash (E. regnans) after Vertessy et al. (1996). That balance of high rates is in contrast to the more moderate rates for "Grass 2" and "Gum 2" (P.G. Slavich, pers. comm., 1996). Simulations were continuous for 1000 years of daily input and output, with soil water movement solved on smaller time steps as needed for numerical accuracy and convergence. Results can be interpreted as multiple realisations of shorter time series, each with a random, but physically realistic initial condition for the first day of the year. In addition to daily output from MWGEN (i.e., rainfall occurrence and amounts, maximum and minimum temperatures, and global solar radiation), WAYES uses vapor pressure deficit (vpd), and computes vapor pressure internally. Monthly mean values ofvpd were computed from humidity and temperature data at nearby Cape Moreton (Bureau of Meteorology, 1988). These values were used in lieu of daily vapor pressure data.

Caveats The method outlined here may provide a qualitative guide to the direction of change and the potential significance of changes in evapotranspiration, soil moisture, and groundwater recharge regimes. As with any modelling exercise or numerical experiment, the results are only indicative of the potential responses of real-world (i.e., complex and uncertain) systems. In addition, there are a number of important qualifications to this assessment of the impact of climate change on groundwater resources beneath North Stradbroke Island: • Climate change scenarios derived from one GCM are uncertain based on the present disagreement among GCMs. A complete study of climate change impacts would include the use of results from several GCMs (lPCC, 1990) . • As noted by Whetton et al. (1995), the present generation of GCMs do not explicitly simulate the El Nino - Southern Oscillation (ENSO), tropical cyclones, rain depressions or extra-tropical lows, and cold fronts. These are the main sources of widespread heavy rain at Stradbroke and are the major source of groundwater recharge events (Fleming, 1995).

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GREEN ET AL.

Table 2. Input vegetation parameters for the W AYES model for two perennial grasses and two types of eucalyptus trees. Model Parameter Description

Vegetation Type Units

Symbol

canopy albedo rainfall intercept. coeff.

m d- I LAI-I

Xr

Grass 2 0.85

Gum

1 0.9

Gum 2 0.9

0.0003 0.0003 0.00025 0.0003 0.65

0.42

0.40

0.0125 0.015

0.025

0.010

10.

0.7

8.0

8.0

wh

200. 2.13

100. 2.13

250. 2.1

250. 1.13

wn

0.5

0.5

0.3

0.3

del C

Topt

26

25

26

25

deg C

Thalf

20

18

20

15

I-lmol m- 2 d- 1 Lmax

1200

1200

1000

1200

light extinction coeff. maximum rate of kg carbon assimilation slope in stomatal conductance model max. plant available water potential m IRM weighting for light IRM weighting for nutrients optimum temperature

Grass 1 0.9

m- 2 d- I Amax al

LWPmax

0.65

half optimal temperature saturation light intensity maximum rooting depth leaf respiration coeff.

m

RDmax

1.5

1.5

10.

15.

kg kgl d- I

RoL

0.003

0.003

0.0014

0.0008

root respiration coefI

kg kgl d- I

RoR

0.0004 0.0004 0.0008

0.0002

leaf mortality rate

kgkgl d- I

mL

0.007

0.001

0.006

0.002

aerodynamic resistance

s m- I

50.

50 .

20.

20.

• An important assumption in this study is that the plant-growth model is able to simulate water use under climatic conditions that are different to those for which the model has been calibrated. Despite this potential limitation, the effect of possible changes in plant transpiration rates and vegetative cover due to CO 2 doubling has been considered.

194


RESULTS Climate Generation Climate scenarios are in the fonn of 1000 years of daily weather data generated using MWGEN; this in tum drives the soil-vegetation infiltration model WAYES to assess potential impacts on groundwater recharge. Sample probability distributions for the daily meteorological variables are shown as box plots in Figure 1. Extreme values in the tails of the distributions (sample size = 365000) are plotted as horizontal lines beyond the whiskers at 1.5 times the interquartile ranges. Simulated changes in maximum and minimum daily temperatures are approximately 5 °C for the median values, and the spread (variance and skewness) is similar for both climates. A significant increase of 37 percent in the mean rainfall amount is masked in this figure by the upper tails. The parent rainfall amount distribution for wet days is represented by a highly skewed Gamma distribution. Occurrence of either wet or dry days is determined by a lag-l Markov model (see Bates et at. (1994) for details), resulting in the histograms of wet- and dryperiod durations shown in Figure 2. The semi-log scale emphasises the projected increase of long-duration periods of both continuous rain ("wet") days and drought ("dry"). A frequency of one would equal zero on the log scale, so a small positive value is added for plotting purposes. Wet period durations of more than 6 days and drought durations of more than 20 days are more frequent for double-C02 conditions ("Climate 2") than for present conditions ("Climate 1If). The maximum wet and dry period durations are projected to increase from 40 to 56 days and from 87 to 109 days, respectively. Recharge Simulations Simulated transpiration and the resulting long-tenn average value of groundwater recharge ("drainage" below the root zone) change disproportionately with climate. The net recharge can more than double with an increase in mean annual rainfall from 1.13 m to 1.56 m (37%) and a mild increase in the average number of wet days (from 39% to 41%). This result is primarily related to the increased frequency of long-duration wet and dry periods (Fig. 2). Figure 3 shows one-decade windows of monthly aggregated rainfall, plant growth and hydrologic response during periods containing the longest simulated droughts for both climate scenarios. Only two of the four simulated vegetation types (Grass 2 and Gum 1) are shown for example. During long dry spells, plants die back, reducing their leaf area and root density. This decreases the transpiration from subsequent infiltration events compared with that of healthy plants experiencing similar water availability. Thus, more water is allowed to drain beyond the root zone. Longer wet periods and increased daily rainfall amounts also enhance the recharge volume and peak drainage rates.

195

GREEN ET AL.

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Figure 1. Daily climatic inputs for Climates I (present) and 2 (double C02): (a) global radiation, (b) maximum temperature, (c) minimum temperature, (d) rainfall amounts. Mean monthly values of vapor pressure deficit were used.

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(a)

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Figure 2. Frequency histograms of (a) dry period durations and (b) wet period durations,

196


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(b) Climate 2

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97

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99 100

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Time (yeare)

Figure 3. Time series (monthly aggregates) of input flux (rainfall), output fluxes (transpiration, total E.T., deep drainage), and leaf area for one decade including the longest drought period for (a) current-C02 and (b) double-C02 conditions.

Recharge rates are simulated daily but vary much more gradually than the intermittent rainfall which drives the unsaturated flow system. This dampening of the drainage response at depth is caused by the flow and storage capacity of the

GREEN ET AL.

197

vadose zone, and thus depends on the depth where deep drainage (Le., water that will become groundwater recharge at the water table) is simulated. That depth is 5 m for the grasses and 25 in for the trees. Although it is not possible to compare daily rates at depth, long-term recharge volumes can be compared. Figure 4 shows box plots of 1000 annual "recharge" values for each combination of vegetation and soil types. Climates 1 and 2 are plotted side-by-side in each soil-type panel to illustrate the pronounced increases in median annual recharge and the increased deviation in annual values. For both climates and with every type of vegetation, the median annual recharge decreases as the soil texture gets finer. Also, the variance tends to decrease with soil texture. (b) Grass 2

(a) Grass 1

o

M.Sand F.Sand

Loam

S.Loam C.Loam

(c) Eucalyptus 1

M.Sand F.Sand

Loam

S.Loam C.Loam

M.Sand F.Sand

Loam

S.Loam C.Loam

(d) Eucalyptus 2

M.Sand F.Sand

Loam

S.Loam C.Loam

Figure 4. Box plots of annual recharge amounts for 1000 years of simulations showing current and double C02 conditions side-by-side in panels for each soil type (Medium Sand, Fine Sand, Loam, Sandy Loam, Clay Loam). Vegetation type is also indicated for each plot (a)-(d). Median values are in white with lower (25%) and upper (75%) quartiles shaded dark and light, respectively. Whiskers span 1.5 times the inter-quartile ranges or to the most extreme (maximum or minimum) value, and extreme values beyond 1.5 whiskers are plotted as horizontal bars.

198


The general pattern of increased temporal variation under Climate 2 may be related to both the increased average annual rainfall amount ("climate change") and to the variations in rainfall occurrence and associated duration frequencies ("climate variability"). Climate change increases the average water content of the soil profile, which decreases the system response time, particularly for high recharge events. Climate variability affects within-year wetness patterns, particularly following droughts or long periods of consecutive rain days; this has a nonlinear effect on vertical water flow and extraction by roots, as well as dynamic vegetative cover and associated transpiration demands. Measures of Climate Impacts on Groundwater Recharge Two measures of the relative increase in recharge highlight the hydrologic response. The first, 021D1 = (Recharge 2)/(Recharge 1), is simply the net recharge under Climate 2 normalised by the net recharge under Climate 1 for each soil-vegetation combination. Given the 37 percent increase in mean annual rainfall, values greater than 1.37 indicate that a disproportionately high amount of rainfall from Climate 2 becomes recharge relative to the Climate 1 scenario. The second measure is, ~O

02-01

.

~ = R2 _ RI == ~(Recharge y~(Rainfall)

(1)

where 01 and 02 are the total drainage volumes (net recharge), and R1 and R2 are the total rainfall volumes under Climates 1 and 2, respectively. A value of zero would mean that all of the additional rainfall is transpired or evaporated (no additional recharge), a value of one means none of the additional rainfall is transpired (on average, it all becomes recharge), and values greater than one mean that the cumulative recharge volume increases more than the increase in rainfall. We use these two measures to summarise the results of two simulation runs having identical soils and vegetation, but with different climates. Figure 5 shows the results for various combinations of five soil types and four types of vegetation. The first measure, 021DI, ranges from 1.74 to 5.09, consistently increasing in value from coarse to finer textured soils and from "Gum 2" (row 4) up to "Grass 1" (row 1). There is a general increase in sensitivity to climate change going from trees to grass, as well as a consistent increase from vegetation with moderate assimilation and respiration rates to those with a balance of relatively high rates. The latter balance yields greater temporal variation in plant biomass, including leaf area. More dynamic vegetation are more susceptible to extended wet and dry periods (climate variability) associated with the climate change scenario.

GREEN ET AL.

1

1

Q)

$ ~ 0

199

Q)

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2

~ 0

:g...,

:g...,

~

~

3 ~

2

~ 3

4

4

1

3

2

4

5

1

Soil Type

1

2

3

4

5

1.0

1.1

Soil Type

4

(Recharge 2) / (Recharge 1)

(A)

3

2

5

0.7

0.8

0.9

d(Recharge) / d(Rainfall)

(8)

Figure 5. Matrix plots of ratios of (a) net drainage for double- divided by current-C02 conditions ("Measure 1") and (b) the change in total rainfall to change in net recharge ("Measure 2", Eq. 2) versus vegetation and soil types. (Vegetation types: l="Grass 1", 2="Grass 2", 3="Gmn 1", 4="Gmn2" ; Soil types: l="Medimn Sand", 2="Fine Sand", 3= "Loam" , 4="Sandy Loam", 5="Clay Loam")

For the second measure (see Fig. 5b), there is no trend with soil type, and the principal differences (in a range of 0.76 to 1.05) are related to vegetation. The alternating bands of dark and light indicate that it is not a distinction between grasses and trees. Rather, it appears to relate to the magnitude of carbon assimilation and plant respiration (see Table 2), and the associated dynamics ofleaf area and plant available radiation. The effects of dynamic versus static vegetation were tested on the trees by rerunning the model with constant leaf area index, root and stem carbon set to average values of the dynamic time series. These runs were limited to 100 years, which is sufficient for testing differences in mean recharge rates. Values of LIDILlli. decreased consistently by approximately 0.1, indicating that vegetation dynamics are responsible for making LIDILlli. exceed 1.0, but other factors affect the first-order difference between vegetation types.

200


Temporal Persistence in Annual Recharge Variability In addition to the statistics of annual recharge discussed with reference to Figure 4, water resource managers are concerned with deficits or surpluses from a given demand. Here, we use the long-term average which equals the sustained yield for both anthropogenic and natural water uses, including discharge to the ocean. Residual mass curves are used to quantify the temporal persistence of deviations for every soil-vegetation-c1imate combination. Example plots for each climate are shown for Gum 2 and Fine Sand in Figure 6. These plots include annual recharge rates, a lO-year running average, and cumulative running average ("Net") recharge, and the annual residual mass curve. The residual mass is defined as,

i t -) j Sj =.1: ,qi- q =.1: qi- joq , j =l,ooo,n

(2) 1=1 1=1 where qi is the recharge rate (mm p.a.) in year i, is the average (net) recharge (mm p.a.) after n years, and n is 1000 years in this case. The range in residual mass over a time series has been used in computing a measure of the persistence in variations from the mean behavior. Most notably, the Hurst coefficient, H (Hurst, 1951; Salas, 1992), can indicate long-term dependence or persistence when H >0.5. We computed estimates H for each soil-vegetation-climate scenario. For Climates 1 and 2, values fall in the ranges 0.585 < H < 0.713 and 0.546 < H < 0.614, respectively. Although these ranges overlap, there is a consistent decrease in the value of H for any given soil-vegetation environment. This indicates a general decrease in the persistence of deviation from the mean recharge. A simple measure of persistence in annual recharge variability is the normalised range ofresidual mass:

q

P = Rn

+q= (max {s'j }- min {s'j })+ q

Vj

~n

(3)

This persistence measure, P (PI and P2 for Climates I and 2, respectively), is the ratio of a volume per unit area (mm) to an annual recharge rate (mm p.a.), yielding units oftime (years). The variation in PI with vegetation and soil types is shown in Figure 7. For Climate I, the range is: 11 < PI < 67 years. By contrast, for Climate 2, the range is much smaller: 6 < P2 < 14 years (not shown graphically). The average persistence for Climate 2 is much less than that for Climate 1, despite the increased variation in annual recharge under dOl,lble-C0 2 conditions. Figure 8 shows the resulting ratio of PI to P2, which is always greater than 2 and approaches a value of 5. There are two possible factors contributing to the significant difference in persistence of recharge between the two climates. First, Rn is normalised by the mean recharge, which is significantly greater for Climate 2, thus compensating for the somewhat greater ranges in residual mass. Second, the same physical mechanism (Le., increased water contents in the soil profile) that caused the variance in annual recharge to increase also decreases the persistence of such deviations from the mean. There is a nonlinear increase in unsaturated

GREEN ET AL.

201

hydraulic conductivity and decrease in soil-moisture deficit with increased water content; this reduces the system response time and associated temporal persistence. lxC0 2: Eucalyptus 2, Fine Sand 3000

800.-------------------------~~

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(A)

400

600

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1000

800

Time (years)

2xC0 2: Eucalyptus 2, Fine Sand 1400

l-yr --lO-yr --Net

ni

3000

Po 1200

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(J)

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(J)

2000

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1000 1000

.... s:

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