Differences in future recharge estimates due to GCMs, downscaling methods and hydrological models

Transcription

1 GEOPHYSICAL RESEARCH LETTERS, VOL. 38,, doi: /2011gl047657, 2011 Differences in future recharge estimates due to GCMs, downscaling methods and hydrological models Russell S. Crosbie, 1 Warrick R. Dawes, 2 Stephen P. Charles, 2 Freddie S. Mpelasoka, 3 Santosh Aryal, 2 Olga Barron, 2 and Greg K. Summerell 4 Received 1 April 2011; accepted 9 May 2011; published 14 June [1] The impact of climate change upon groundwater has an increasing profile in the literature but there is little guidance on selecting Global Climate Models (GCMs), downscaling methods or hydrological models. This paper quantifies the relative uncertainties inherent in projections of future recharge contributed by multiple GCMs, downscaling methods and hydrological models at three locations across southern Results highlight that the choice of GCM is the largest source of uncertainty, with a median range between the highest and lowest GCM of 53% of the historical recharge for a given downscaling method and hydrological model. The downscaling method is the next largest source of uncertainty with a median range of 44% and the choice of hydrological model is the source of the least uncertainty with a median range of 24%. These results strongly suggest that impact studies should use multiple GCMs and give careful consideration to the choice of downscaling methods. Citation: Crosbie, R. S., W. R. Dawes, S. P. Charles, F. S. Mpelasoka, S. Aryal, O. Barron, and G. K. Summerell (2011), Differences in future recharge estimates due to GCMs, downscaling methods and hydrological models, Geophys. Res. Lett., 38,, doi: /2011gl Introduction [2] There is a small but growing body of literature investigating impact of projected climate change upon groundwater to support water resources management. There is a common approach that most studies have followed: (1) obtain data from GCM(s), (2) downscale climate data to a scale suitable for hydrological modelling, (3) run future climate time series through hydrological model to estimate recharge, and (4) use new recharge in groundwater model for evaluation of management scenarios. Each of these processes requires choices about the models or techniques to be used, often with limited or no justification as to why these choices were made. [3] Even very recently, many studies have only used a single GCM [Austin et al., 2010; Holman et al., 2009; Mileham et al., 2009; Toews and Allen, 2009; van Roosmalen et al., 2009]. There has been little justification on why that particular GCM was chosen, but it is often a pragmatic decision based upon 1 CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, Glen Osmond, South Australia, 2 CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, Wembley, Western Australia, 3 CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, Canberra, ACT, 4 Department of Environment, Climate Change and Water, NSW Government, Wagga Wagga, New South Wales, Copyright 2011 by the American Geophysical Union /11/2011GL the country where the researchers were based (e.g., UK based researchers have used HADCM3, Canadians have used CGCM3.1 and Australians have used the CSIRO GCM). In contrast, some researchers have used as many GCMs as they could get access to and some have found large differences in recharge projections cross the range of GCMs used [Crosbie et al., 2010; Eckhardt and Ulbrich, 2003; Loáiciga et al., 2000]. [4] There have been several recent reviews of the different methods of downscaling from GCM to local scale [Fowler et al., 2007; Maraun et al., 2010; Wilby et al., 2004]. However, there have only been two comparison studies of downscaling impacts on recharge [Holman et al., 2009; Mileham et al., 2009], both concluding that the simplest method (change factor) is inappropriate and more rigorous methods should be used. For example, Mileham et al. [2009] showed that the choice of downscaling method could result in 50% increase or decrease in recharge using the same GCM and hydrological model. [5] A variety of hydrological models have been used with downscaled climate sequences to produce recharge projections under a future climate. These range from simple models of a conceptual nature (e.g., HELP [Schroeder et al., 1994]) to physically based models that simulate plant growth and unsaturated movement of water (e.g., WAVES [Zhang and Dawes, 1998]). These model types have not been compared for estimating the impact of climate change on recharge. [6] This paper aims to explore the uncertainty that arises through the choice of GCMs, downscaling methods and hydrological models in projecting the impact of future climate on groundwater recharge. This will be undertaken using a single SRES emissions scenario (A2) for a twenty year period centred on 2055 at three locations in Australia with contrasting recharge regimes. 2. Methods [7] The GCMs used were the CSIRO Mk3.5, GFDL 2.0, GFDL 2.1, MIROC 3.2 medres & MPI ECHAM5, they were selected because (relative to the other GCMs) their simulations performed well when compared to the historical climate of the Australian region [Vaze et al., 2011]. Two 20 year periods were considered for analysis: the historical climate represented by 1981 to 2000 and a future climate represented by 2046 to 2065 under the SRES A2 scenario (a relatively high emissions scenario). [8] The three methods used for spatial downscaling from the GCM grid scale to a point scale for use in the hydrological models were a daily scaling approach, a stochastic downscaling approach and a dynamic downscaling approach. The daily scaling approach is an evolution of the pattern scaling 1of5

2 Figure 1. Boxplots of the change in rainfall projected at three sites for the SRES A2 scenario comparing the differences in GCMs and downscaling methods. The corresponding points are also shown. approach that has an added step to scale the daily rainfall PDF for the changes in rainfall intensity [Mpelasoka and Chiew, 2009]. The stochastic downscaling (ST) approach used is the Nonhomogenenous Hidden Markov Model [Charles et al., 1999; Hughes et al., 1999]; this model uses atmospheric predictors from the GCM outputs to produce multiple realisations of climate time series. The dynamic downscaling approach proposed was CSIRO s Conformal Atmospheric Model (CCAM) [McGregor, 2005], however its output could not be used directly due to biases between the grid scale of CCAM and the point scale of the observed data. Therefore the daily scaling approach was applied using scaling factors derived from the changes projected by CCAM. [9] The daily time series (rainfall, temperature, vapour pressure deficit and solar radiation) from the downscaling methods and the observed historical climate were used as inputs to four hydrological models: WAVES with dynamic vegetation growth (WAVES G) [Zhang and Dawes, 1998]; WAVES with a constant annual pattern of LAI (WAVES C); HELP [Schroeder et al., 1994] and SIMHYD [Chiew et al., 2002]. These models were chosen because they have previously been used in climate change impact studies and represent a range of process sophistication in their conceptualisation. WAVES G is a physically based model that uses Richard s equation to distribute moisture through the soil profile, carbon assimilation and respiration of roots is determined using an empirical relationship and the energy balance is solved using the Penman Monteith equation. WAVES C is a simplified version of WAVES G where the LAI is specified rather than being dynamically calculated. HELP is a bucket model where water is routed down through multiple soil layers to become recharge; it was originally developed for the evaluation of landfill performance but has been used extensively for estimating recharge. SIMHYD is the simplest model considered here and is a lumped conceptual model primarily designed for simulating runoff. [10] The four models were used at three contrasting locations: Gnangara in Western Australia (31.75 S, E); Moorook in South Australia (34.28 S, E); and, Livingstone Creek in New South Wales (35.12 S, E). Gnangara is characterised by a temperate climate (Koppen Geiger class Csa), deep sandy soils and very high recharge [Sharma et al., 1991]. Moorook has an arid climate (Koppen Geiger class BSk), deep sandy soils and low recharge [Cook et al., 2004]. Livingstone Creek has a temperate climate (Koppen Geiger class Cfa), and the only one of the three sites that has any runoff [Summerell et al., 2006]. Each hydrological model was calibrated to field data from each site using the historical climate time series and perennial grass as the vegetation type. [11] To simplify the comparison of modelling results we will focus on the change in mean annual rainfall and recharge between the historical period ( ) and the future period ( ) for the different GCMs, downscaling methods and hydrological models. 3. Results [12] At Gnangara, all models estimated that around 50% of the observed annual average ( ) rainfall of 762 mm became recharge with negligible runoff. At Moorook, all models estimated around 5% of the annual average rainfall of 256 mm became recharge with negligible runoff. At Livingstone Creek, all models estimated around 5% of the annual average rainfall of 603 mm became recharge with substantially more as runoff. [13] Downscaled results at each location produce a wide range of projected rainfall changes, predominantly due to the large range in daily scaled results (Figure 1). Across the five GCMs and three downscaling techniques at Gnangara, the future rainfall projection changes (relative to ) ranged from a minimum of 32% to a maximum 2of5

3 Figure 2. Boxplots of the change in recharge projected at three sites for the SRES A2 scenario comparing the differences in GCMs, downscaling methods and hydrological models. The corresponding points are also shown (there are two points cut off the plots at Moorook, +218% and +447%, for ECHAM Daily WAVES C and HELP3 respectively). of +7% with a median of 18%. At Moorook the future rainfall projections ranged from 35% to +18% with a median of 11%. At Livingstone Creek the future rainfall projections ranged from 32% to +8% with a median of 13%. The GFDL 2.0 GCM projected the lowest median future rainfall at each site but the highest median future rainfall was projected by a different GCM at each site. There were consistencies evident in the downscaling where the stochastic downscaling gave the lowest median rainfall change at each location and CCAM gave the highest median rainfall change at two out of three sites. The daily scaling gave the greatest range between maximum and minimum rainfall projections at all three sites. [14] The change in recharge projected under a future climate shows an amplification of the change in rainfall results (Figure 2). At Gnangara the range of recharge projections across all GCMs, downscaling methods and hydrological models was from 55% to +17% with a median of 31%. At Moorook the range of recharge projections was from 83% to +447% with a median of 23%. At Livingstone Creek the range of recharge projections was from 68% to +101% with a median of 7%. [15] Consistent with the rainfall projections, the lowest median projection of recharge from each site was produced from the GFDL 2.0 GCM with a different GCM producing the highest median projection of recharge at each site. A metric of the uncertainty in the projections from the different GCMs can be calculated as the difference between the maximum and minimum of the future recharge projections made by a given downscaling method and hydrological model across all GCMs at each site. The median of this range between GCMs (GCM uncertainty) was 53% of the historical recharge. [16] The downscaling methods were more consistent than the GCMs when comparing the change in recharge projections across the sites. At Gnangara and Livingstone Creek the recharge projections mirrored the rainfall projections with the daily scaled CCAM results giving the highest median recharge and the stochastic downscaling projecting the lowest median recharge. At Moorook this pattern was inconsistent as the daily scaling gave the highest median recharge projection. The uncertainty due to the downscaling method was quantified as the difference between the maximum and minimum of the future recharge projections made by a given GCM and hydrological model across all downscaling methods at each site. The median of this range between downscaling methods (downscaling uncertainty) was 44% of the historical recharge. [17] The projection uncertainties between the hydrological models were not as large as those between the GCMs or downscaling methods. The models were not always consistent across sites with HELP providing the lowest median recharge in two out of three sites and WAVES G providing the highest median recharge in two out of three sites. The 3of5

4 uncertainty due to the hydrological models was quantified similarly to the GCMs and downscaling methods and was found to be 24% of the historical recharge. 4. Discussion [18] Using four or five of GCMs is a big advancement on using a single GCM in terms of being able to quantify the uncertainty in recharge projections under a future climate [Allen et al., 2010; Ng et al., 2010]. However, choosing only the best performing GCMs may not result in a narrowing of the range of projections, and choosing the extremes of the GCMs for rainfall projections may not produce the extremes of the recharge projections (R. S. Crosbie et al., Episodic recharge and climate change in the Murray Darling Basin, submitted to Hydrogeology Journal, 2010). This suggests that the best option for selecting GCMs is to use as many as possible, this has the added benefit of opening up the possibility of providing recharge projections in a probabilistic framework [Crosbie et al., 2010]. [19] The two previous studies that have compared downscaling methods for their effect on recharge have compared the change factor method to a more sophisticated method [Holman et al., 2009; Mileham et al., 2009], both of these studies concluded that the change factor method of downscaling should not be used because it does not account for higher rainfall intensities or have the ability to produce different temporal sequencing. The three downscaling methods used here are more sophisticated than the change factor approach but do vary in their results. The rainfall output from GCMs and RCMs is generally acknowledged as biased [Fowler et al., 2007; Maraun et al., 2010], but it was used here in the GCM and CCAM daily scaling. The stochastic downscaling is different in that it used atmospheric predictors that are less biased than the rainfall from GCMs. The stochastic downscaling also has the added advantage of producing changes in sequencing through multiple realisations of a future climate time series [Holman et al., 2009]. [20] A physically based hydrological model that can incorporate plant physiological responses of elevated CO 2 and temperature on carbon assimilation should provide a more realistic projection of recharge under a future climate, but this comes at a cost with the computational time and the uncertainty associated with additional parameters. McCallum et al. [2010] showed that recharge projections from the WAVES model (WAVES G) were affected by changing CO 2 and temperature, but the results of the modelling here did not show significantly different recharge projections compared to the simpler WAVES C model. The HELP model performed erratically at the arid Moorook location, the other three models were more consistent. 5. Conclusions [21] Projections of recharge under a future climate are uncertain. Even without considering different emission scenarios or time periods, mid century projections of the change in recharge range from 55% to +17% at Gnangara, 83% to +447% at Moorook and 68% to +101% at Livingstone Creek when using five GCMs, three downscaling techniques and four hydrological models. The greatest source of this uncertainty is the difference in the climate projections from the five GCMs considered. The downscaling methods investigated also contributed to the uncertainty in recharge projections, there were trends observed across all three sites with the daily scaled CCAM output generally giving the highest rainfall and recharge and the stochastic downscaling the lowest rainfall and recharge. The four hydrological models were different but not consistently across sites, contributing less uncertainty than the GCMs or downscaling methods. This conclusion that the uncertainties in recharge projections are dominated by the GCMs and that the choice of hydrological model is the least source of uncertainty is consistent with runoff modelling [Chiew et al., 2009; Chiew et al., 2010; Teng et al., 2011]. The results shown here provides evidence that multiple GCMs should be used in any impact study and consideration should also be given as to whether multiple downscaling methods be used. Without the use of multiple models there is no accounting for the large potential uncertainties in future recharge estimates. [22] Acknowledgments. The project team would like to acknowledge the National Water Commission for providing funding to support the current studies under the National Groundwater Action Plan. CMIP3 for providing GCM outputs. 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