Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 2 Sciences Discussions Earth System Hydrology and , S. C. Corney 2 1,2,6 ff , M. R. Grose 4 , and N. L. Bindo 1784 1783 6 , D. A. Post 3 , J. J. Katzfey 2 , F. L. N. Ling 1,2,3,* , G. K. Holz 5 This discussion paper is/has been under review for the journal Hydrology and Earth System Sciences (HESS). Please refer to the corresponding final paper in HESS if available. Institute for Marine and Antarctic Studies (IMAS), University of Tasmania, Antarctic Climate and Ecosystems Cooperative Research Centre (ACE CRC), Entura, Cambridge Park, Tasmania, Australia Commonwealth Scientific and Industrial Research Organisation (CSIRO) Land andDepartment Water of Primary Industries, Parks, Water and Environment, Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric now at: CSIRO LW, Highett, Victoria, Australia Received: 16 December 2011 – Accepted: 27Correspondence January to: 2012 J. – C. Published: Bennett 8 ([email protected]) February 2012 Published by Copernicus Publications on behalf of the European Geosciences Union. Hydrol. Earth Syst. Sci. Discuss.,www.hydrol-earth-syst-sci-discuss.net/9/1783/2012/ 9, 1783–1825, 2012 doi:10.5194/hessd-9-1783-2012 © Author(s) 2012. CC Attribution 3.0 License. B. Graham High-resolution projections of surface water availability for Tasmania, Australia J. C. Bennett 1 Sandy Bay, Tasmania, Australia 2 University of Tasmania, Sandy3 Bay, Tasmania, Australia 4 (LW), Black Mountain,5 Australian Capital Territory, Australia New Town, Tasmania, Australia 6 Research (MAR), Aspendale,* Victoria, Australia Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3 %) − = is generally ff 22 %). We find the hydrological − = 1786 1785 in central Tasmania, while runo ect surface water availability, RCM outputs are ff ff The challenge in coupling RCMs directly to hydrological models is that RCM outputs There is much more variation in projections between RCM simulations than between We use the SIMHYD model to describe future changes to streamflow in eight rivers. This is the first major Australian study to use high-resolution bias-corrected rainfall The performance of the hydrological models is assessed against 86 streamflow usually do not match observations accurately enough to allow hydrological models to point out that indirectrainfall coupling variability methods often or do tothese not changes are explicitly in likely account to the forhydrological have changes sequences models significant to of has impacts the wet on advantagejected and streamflow. that by dry the Coupling RCMs, complex days, RCMs including suite directly evenand changes of to though number to rainfall changes seasonal of pro- rainfall, rainmore maximum meaningful days, daily assessment will precipitation, of bevariability. climate reflected change in impacts projections on streamflow of volumes streamflow. and This allows how these complex rainfall changesoften a coupled to hydrological models.indirectly by RCMs adjusting and historical hydrological models observationsal., can to 2009), be resemble or coupled the directly future(Akhtar by climate et using (Chiew al., timeseries et 2009; generated Kilsby by et RCMs al., in 2007; hydrological Wood models et al., 2004). Fowler and Kilsby (2007) Human-induced climate change has been showndistribution to of contribute precipitation to in changes in theworld, the 20th understanding spatial century the (Zhang local et andcycle al., regional 2007). is implications of In critical changes a to ingional warmer the planning climate future hydrological models for (RCMs) water haveimpacts security been on used (Oki spatial successfully and to distributions Kanae,(Kendon assess of climate 2006). et rainfall change al., (Kilsby, Dynamical 2007),quency 2010), re- seasonal (Mailhot and changes et changes to al., rainfall 2007) to at rainfall spatial intensity scales relevant (Berg to et water al., managers. 2009) To assess and fre- allowing for increased confidenceability. in assessing future changes to surface water vari- 1 Introduction Our study shows that these simulations are capable of producing realistic streamflows, and potential evapotranspiration projections as direct inputs to hydrological models. simulations than the rangeuncertainty of in hydrological the projections. models used here to adequatelyChanges describe to streamflowsto are 30 projected % to areprojected projected vary to for increase by in annual region. the runo east.most Daily of streamflow Marked Tasmania, variability consistent decreases is with projectedof increases of to streamflows in increase up is rainfall for intensity. projected Inter-annual to variability increase across most of Tasmania. models that best simulatetions. In observed contrast, streamflows the poorly producethe performing other similar IHACRES hydrological model streamflow models. amplifies projec- changes more than hydrological models. This shows that it is more important to consider the range of RCM and potential evapotranspiration aremospheric generated Model with (CCAM), the a variable-resolution CSIROvariables regional Conformal are climate Cubic model bias-corrected (RCM). At- withhydrological These models quantile AWBM, IHACRES, mapping Sacramento, SIMHYD andstreamflows. and used SMAR-G to as project direct inputs to the gauges across Tasmania. The SIMHYD modelwhile is the IHACRES least has biased the (median bias largest bias (median bias Changes to streamflows causedmanagement by of climate water for changetralia. hydro-electric may generation have We and present major agriculture high-resolutionbetween impacts in 1961–1990 projections Tasmania, on and Aus- of 2070–2099. the Six Tasmanian fine-scale surface (10 water km) simulations availability of daily rainfall Abstract 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ´ e ´ e et al., 2007) has ective at removing biases ´ e et al., 2007; Fowler and ff under the median scenario by in Tasmania’s central highlands . Post et al.’s (2012) median fu- ff ff ff ) and most southerly state, in addition 2 1788 1787 70 000 km ∼ ective for coupling climate models and hydrological models (Bo ff This paper’s primary aim is to quantify seasonal and spatial changes in Tasmanian Finally, this paper aims to understand whether uncertainty in the streamflow projec- Tasmania’s highly varied rainfall distribution is poorly replicated by GCMs, making Longer-term ensemble GCM projections of rainfall change for Australia to 2100 by Quantile mapping (also called quantile-quantile bias-correction; Bo Tasmania is Australia’s smallestto ( being Australia’s only island state. Tasmania is mountainous, with mountain ranges projections is less common,contribute even significantly though uncertainties to in uncertaintiesal., hydrological 2011). in modelling To climate may find change ifhydrological the models, impact RCM we studies simulations couple (Bastola arehydrological an models. a et ensemble greater of source of RCM uncertainty simulations than to the an ensemble of 2 Study area: Tasmania historical streamflows. tions comes more from the RCMpractice simulations of than from using the ensembles hydrologicalis modelling. of well The climate established. models Using ensembles to of describe hydrological uncertainty models in to quantify projections uncertainty in the 21st century. streamflows by 2100 usingresolution study high-resolution of RCM changes inTo simulations. better Tasmanian streamflows understand by This future the isusing changes end in bias-corrected the of streamflow the RCM first 21st variability, projectionsis high- we century. as the project direct streamflows first inputs Australianprojections, to study and accordingly hydrological to we models. aim use to this demonstrate Ours that method our to method credibly produce replicates basin-scale surface water region was projected2030. to experience Post et increased al.ture runo (2012) in note Tasmania in that light theresouth-west of are declining Western plans agricultural Australia. to yields Longer-term develop inter new the high-resolution availability irrigation Murray projections Darling are infrastruc- of basin needed surface and wa- for informed water management planning in Tasmania for and north-eastern highlands of up to 30 % by 2030, with little change elsewhere. No ture scenario projected decreased mean annual runo midway between a region ofdecreasing increasing precipitation precipitation to to the theChristensen south-east north-west. et and al. a (2007) About project region half increased of of mean annual the precipitation 21Tasmania for an GCMs Tasmania. ideal described candidate by forof fine-scale a modelling. major Tasmania has hydroclimatologicalability been study of the by subject surface Post water(Mitchell, et in 2003) al. Tasmania of to (2012) global 2030. thatto climate better models reviewed Post replicate (GCMs) future et spatial and avail- al. variation a in (2012) series Tasmanian of used runo hydrological pattern models scaling 2010; Chiew et al., 2009; Post et al., 2012). the Intergovernmental Panel on Climate Changeinconclusive (IPCC) results (Christensen for et Tasmania. al., Christensen 2007)sign et give and al. magnitude (2007) of find rainfall little change agreement over in Tasmania, the perhaps because Tasmania sits et al., 2007; Woodfrequency et distribution al., of 2004). afrom Quantile given climate mapping variable, model corrects and outputsmapping biases is (Ines has across highly been and the e successfully entire Hansen,eral used 2006; northern to Piani hemisphere couple et RCMs studiesKilsby, 2007; to al., (Akhtar Wood hydrological 2010a). et et models al., al.,cal in Quantile 2004), studies 2009; sev- but in Bo has Australia, where notsimple indirect been perturbation coupling used methods of for based historical regional on observations hydroclimatologi- pattern-scaling have and been more popular (Charles et al., model outputs are often linked toThese hydrological range models from with statistical simple coupling scalingweather methods. to generators more (Fowler complex et methods al., of 2007; bias-correction Maraun such et as al., 2010). been shown to be e produce realistic streamflows (Graham et al., 2007). To address this problem, climate 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1100 mm) in the > the east coast cause ) over Australia. The ◦ ff ´ e and Terray, 2007; Zou et al., 2010). 3000 mm on the western mountains to > 850 mm in the south and west (Fig. 2b). ´ c and Swart, 2000) from stage 3 of the < 1790 1789 , from ff ´ cenovi 1200 mm) occurs. > ( ff er from the problems associated with lateral boundaries in limited area RCMs (Fox- ff For this study, CCAM is configured to be forced only by GCM sea surface tem- Mean annual areal potential evapotranspiration (APET) is highest ( Tasmanian mean annual rainfall follows a sharp gradient from west to east, with 100 mm in some eastern areas (Fig. 2c). An exception to this west-to-east gradient second stage is forced using thetration same along bias-corrected with GCM spectral SSTs nudging and (Thatcher sea-ice and concen- McGregor, 2009) of the atmosphere coupled model intercomparison projectCSIRO-Mk3.5, (CMIP3) (Meehl ECHAM5/MPI-OM, et GFDL-CM2.0, al.,and 2007) GFDL-CM2.1, are UKMO-HadCM3. MIROC3.2(medres) downscaled: Forthe convenience, GCM each used RCM toal., simulation force 2007) will it. are be Before removedto referred downscaling, using to Reynolds biases a by (1988) in simple the SSTs.stage additive GCM is bias-correction SSTs The forced (Katzfey (Randall downscaling etand only et is achieves al., with an carried 2009) approximate the horizontal out bias-corrected resolution in GCM of two 50 SSTs km stages. (0.5 and sea-ice The concentration, first et al., 2008). peratures (SSTs) andwith sea this ice configuration concentration.southern to Africa generate CCAM (Engelbrecht high-resolutionSRES et has regional A2 al., climate been emissions projections 2009). successfully scenario over used (Naki Simulations from six GCMs under the CCAM is a globalolution atmospheric over model Tasmania. thatsu uses CCAM a has stretched no gridRabinovitz lateral to et increase boundaries al., the andshown 2008). res- accordingly to does simulate Variable rainfall not resolution andlocations (Berbery related global and processes atmospheric Fox-Rabinovitz, realistically 2003;CCAM models at Bo a has have range been been of usedChiew scales for et and al., regional 2010; climate Post studies et in al., 2012) Australia and (Charles internationally et (Engelbrecht al., et 2007; al., 2009; Lal 3.1 Regional climate modelling Regional climate simulations are producedthe for CSIRO 1961–2100 Conformal by Cubic downscaling Atmospheric GCMs with Model (CCAM) (McGregor and Dix, 2008). 3 Data and methods central north of TasmaniaThese and patterns declines to ofto-east APET gradient and in rainfall mean< combine annual runo to giveis Tasmania the a small, very mountainous steephigh Ben mean west- Lomond annual plateau runo in the north-east of Tasmania, where contrast to the winterdoes dominant not rainfall show a in strong the seasonaloccasional west cycle. high-intensity and Low pressure rain north-west, systems storms rainfall o and over in less the reliable the rainfall, east east agriculture is of an the important Tasmania. industry in Despite the the lowlands of low the east. austral winter (June-July-August –February – JJA) DJF). and Snowfalls are lowestically common melts in on within Tasmanian summer mountains, a however (December-January- few snowof weeks typ- Tasmanian and streamflows. seasonal snowmelt The ishigh central, not conservation western an value and important and south-western component world much mountains heritage of are area. this of unpopulated region is listed asthe a central UNESCO midlands and eastern lowlands averaging less than 600 mm (Fig. 2a). In all exceeding 1000 m inwinds (Fig. elevation. 1), and Tasmania the lies prevailingTasmania westerly in to weather make the combines the with path mountains western in ofMean part western the annual of “Roaring Tasmania rainfalls one Forties” exceed of4000 mm 2000 the mm on wettest for areas some in much mountain Australia. of peaks the (Fig. west 2a). and rise Rainfall to in more the than west is highest in the in the north-east (Ben Lomond Plateau), centre (central highlands), west and south 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (2) (1) 5 5 . . (RCM) i 99 99 P = b b < : : } } 5 5 . } . 0 5 0 . (Obs) and + + i i 99 P 10 km) gridded obser- , ∼ ≥ 5 ( rey et al., 2001). Rainfall . ◦ b > i b ff 98 ≤ ≤ 5 5 . . 0 0 , ..., 5 − − . i i 1 { { ,  5 . -th percentile, and 0 and i { } 5 = . 1792 1791 i 99 , 5 5 km) SILO dataset (Je . and ∼ 98 ( ◦ 0 , ..., 5 . > 1 , 5 are the uncorrected and corrected simulations, respectively, . 0 0 0 b { = 1). Quantile mapping factors are calculated for each percentile = (RCM) ) over Tasmania. i i = ◦ P i : F : b (RCM) and RCM i b P RCM · (Obs) (RCM) i i is the quantile mapping factor at the i -th percentiles of observation and RCM outputs, respectively. This is similar P F P 1 : i i F =      0 b = Before the bias-correction is implemented, we detrend each season in the uncor- Finally, bias-corrected RCM outputs are regridded from the 10 km RCM grid to a We calculate ‘quantile mapping factors’ independently at each grid cell for each RCM We force any rain day with rainfall of less than 0.2 mm to zero in both observed and i 1–2, ..., 98–99, 99–100), andthen assigned transferred a to rank the that original accordsare to (undetrended) calculated the simulation. for bin. each Bias-corrected These day RCM ranks for outputs are the entire simulationRCM by dataset. rected simulation (1961–2100)any by long-term subtracting a changesconsigned 30-year in to moving rainfall a percentile average regimes. “bin” to between Each remove integer percentiles day (i.e. from percentile bins this of detrended 0–1, series is resolution limit of the Bureau of Meteorology rain gauges that are the basis of the SILO quickflows, which are then combineddistinguished to represent from observed the hydrographs. other IHACRES models is by (i) employing a rainfall scaling parameter and 5 km grid to be compatible with the hydrological3.3 models. Hydrological modelling We use the(Boughton, five 2004), hydrological IHACRESal., models (Post 1973), and calibrated SIMHYD Jakeman, (Chiewand by SMAR-G et 1999), Viney (Goswami al., et Sacramento et al., 2002)models (Burnash 2002). with that al. et The use Muskingum (2009b): hydrological a routing models variety (Tan are of et simple AWBM algorithms conceptual al., to partition 2005), available water into baseflows and applies the quantile mappingcentile factors bin, calculated the at factor the foron the 0.5th 1.5 up percentile percentile to to is the the matchedbin. factor 0–1 to for the per- the 1–2 percentile 99.5th bin, percentile, and which so is applied to the 99–100 percentile where RCM falling in percentile bin b. The other terms are as described for Eq. (1). Equation (2) F use quantile mapping to alignvations daily aggregated rainfalls from and the APET 0.05 tois 0.1 a direct output from(vapour the pressure, SILO temperature dataset, and while solarfor APET radiation) wet is according environments. calculated Morton’s from (1983) base method variables simulation: lution of 10 km (0.1 3.2 Quantile mapping Two inputs are required for the hydrological models: daily rainfall and daily APET. We modelled rain time series. The threshold of 0.2 mm is chosen because it is the lower from the corresponding 50 km simulations, achieving an approximate horizontal reso- where are the to the methodfrequency of distribution, Li however we et calculatetributions. corrections al. from (2010) empirical When frequency in RCMmapping dis- that outputs factor we are ( independently zerofrom 0.5 for correct to Eq. 99.5 moments (0.5th, (1), offrom 1.5th, we ..., all the 98.5th, data, do 99.5th including percentiles). notdependently days Percentiles calculate at of are each calculated zero a grid rain. quantile September-October-November cell Quantile (SON) for for mapping the the factors seasons training are period DJF, calculated March-April-May 1961–2007. in- (MAM), JJA, and 5 5 25 20 15 10 10 20 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) 5 Q ) is assessed 20-year, high- ff > is either observed stream- o Q against observed streamflows. timeseries at a daily time step ff ff and ff ) streamflows at 86 sites. Biases vary 95 . Q ff 1794 1793 erences between simulated mean annual rainfall ff , and give good coverage of Tasmania (Fig. 4). Perfor- against streamflows modelled with hydrological models 2 ff ). Biases are calculated as: % ff × to eight river catchments (Fig. 3). Operation of storages, diver- o 2000 km grid covering all of Tasmania. To achieve Tasmania-wide cover- Q ff > ◦ 1 T = erent climatic regions of Tasmania, and all have P to o t ff 2 Q ects of the RCM inputs on hydrological model performance, biases are − ff 1 T = 20 % for most of Tasmania for all six RCM simulations presented here P m t + Q is streamflow modelled with RCM-runo 1 T = m P t Q = Figure 5 shows biases of mean annual streamflows, biases of 5th percentile ( Changes are described between a baseline period, 1961–1990, and a future period, We aggregate runo Descriptions of streamflow changes in a further 70 Tasmanian rivers, 12 large ir- 15 % and Low variation between RCMGCM SSTs simulations before downscaling, is which caused forces GCM in SSTs part to be by similar to the observations bias-correction of bias where flow or streamflow modelled with SILO-runo streamflows and biases ofmuch 95th percentile more ( between hydrological models than between RCM simulations (Fig. 5). To isolate the e also calculated for RCM-runo forced by SILO (SILO-runo quately during the baseline (1961–1990) and future (2070–2099) periods. 4.1.2 Comparisons of biases of hydrologicalPerformance models of hydrological models forced withat 86 RCM streamflow inputs gauges (RCM-runo for allin data size available for from 1961–2007. 8 The km 86mance catchments is range assessed by calculating biases of RCM-runo annual rainfall was more than 15rainfall % in lower or the more calibration thanand period. 20 SILO % mean greater Di annual than mean rainfall− annual during the calibration period(Bennett (1975–2007) are et between al., 2010), suggesting that the hydrological models should perform ade- Sacramento, SIMHYD and SMAR-G models declined sharply in periods where mean Performance of a hydrological modelmate may (Merz not et remain al., consistent 2011). under a Vaze changing et cli- al. (2010) found that performance of the IHACRES, 2070–2099. 4 Results 4.1 Performance of hydrological modelling 4.1.1 Hydrological model performance under a changing climate quality streamflow records. rigation storages andal. the (2010). Tasmanian hydro-electric system are given by Bennett et streamflows. The hydrological modelsdistributed on produce a runo 0.05 age with the five hydrologicalto models, ungauged Viney et catchments al. from (2009b) their assigned nearest model gauged parameters neighbour. sions and water extractions incurrent these at catchments are 31 accounted December forthey 2007 based represent on (Bennett practices di et al., 2010). The eight rivers are chosen as log-bias objective function (Vineyhydrological et al., models 2009a) to to 90Tasmania. automate streamflow the records calibration The for of streamthat 1975–2007 the had five records for negligible catchments human Viney around influencerecords et on were al. streamflows. augmented For (2009b) with four chose catchments, estimates streamflow were of from irrigation catchments extractions to simulate natural (ii) by characterising streamflow using a unit-hydrograph. Viney et al. (2009b) used a 5 5 20 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ff is 95 ff Q erences tends to ff ff reflect di 25 % for more than ff ± than against observa- ff 22.3 %), and Sacramento − = streamflows as well as higher (black line in Fig. 7), indicating 5 Q ff is compared to daily CV of SIMHYD ff 1796 1795 streamflows (Fig. 5), and has the largest me- to underestimate daily CV of observed runo streamflows (Fig. 5). This is expected as biases 95 ff Q 95 80 % of catchments). IHACRES is least like observed streamflows for 1961–2007 (Fig. 5, Table 1). AWBM, Q > 95 Q matches observed seasonal streamflows reasonably well . The bias-correction aligns frequency distributions of mod- tends to underestimate the daily variance (measured as the 3.2 %) and smallest interquartile ranges of biases of any hy- ff ff − ff = simulations tend not to replicate ff biases are generally smaller against SILO-runo ff at the same sites, there is strong agreement (Fig. 6b). This implies that do not replicate observations well. That is, many of the deficiencies in low 60 % of catchments) and a strong tendency to underpredict observed ff ff > cient of variation, CV) of observed streamflows at the 86 gauge sites (Fig. 6a). ffi SIMHYD RCM-runo SIMHYD RCM-runo All RCM-runo RCM-runo Flows modelled with AWBM, SIMHYD and SMAR-G show similar characteristics to 10 % for more than 40 % of catchments and biases smaller than ference is also presentthat it in is the caused by SIMHYD hydrological SILO-runo model calibration or the SILO rainfalls rather than the not caused by theSIMHYD RCM hydrological or model. the The bias-corrected bias-correction,of RCM variability but inputs to rather reproduce that a by present similar in the SILO level SILO rainfalls for dataset the or purposes(Fig. the of 7). hydrological modelling. Seasonal streamflowsern are catchments, particularly illustrated closely by matched theern in Black and northern River southern and and Rubicon west- catchmentstends River. (Nive, In to Franklin the and underpredict central, Huon west- gauged Rivers) streamflows SIMHYD from RCM-runo September to December. This dif- several additional performance tests of the SIMHYD model here. coe However, when daily CV ofSILO-runo SIMHYD RCM-runo the tendency of SIMHYD RCM-runo lem in hydrological modelling.SILO-runo Figure 5streamflows shows emanate that from the low hydrological streamflows models. generated from 4.1.3 SIMHYD model performance SIMHYD exhibited the lowestfocus biases on of SIMHYD the projections hydrological to models, report and changes accordingly to we future streamflows. We describe eas (Bennett et al., 2011).caused by Underestimation inadequate of streamflows replicationbias-corrected is of RCM therefore the inputs. most temporal probably characteristics of rainstorms by the streamflows (Table 1, Fig. 5). Poor replication of low streamflows is a common prob- bias-corrected RCM rainfalls tend to overestimate large daily rainstorms over large ar- temporal correlations of rainfall (how rainfalls behave in a multi-day rainstorm). The seasonal streamflows (Table 1). tions for mean streamflowscalculated and against observations add errorshydrological in models, the while RCM inputsonly biases to between calculated errors the inherent against in RCMunderpredict SILO-runo the inputs SILO-runo and SILOelled variables. and In observed general,rainstorms rainfalls, (how RCM-runo however daily it rainfalls does in all not grid account cells for in a spatial catchment correlations behave of together) nor for streamflows (underpredicted in streamflows (median bias forbiases mean are annual second streamflows largest afterunderpredict IHACRES. IHACRES observed shows mean a very and dian strong biases tendency to and largest interquartile ranges of biases against observed annual and observed mean annual and SIMHYD and SMAR-G replicate± observed streamflows well, with85 % biases of smaller catchments. than SIMHYDannual has streamflows the smallest mediandrological biases model (median for annual bias and forSMAR-G seasonal mean streamflows show (Table 1). a AWBM, tendencydicted SIMHYD to and in underpredict observed annual streamflows (underpre- consistent with the usemance of of hydrological a models single tendsfocus RCM not on to for describing hydrological vary all model greatly the biasesfrom between for here simulations. RCM the on. simulations, mean Because we of the the six perfor- RCM simulations for 1961 to 1990 (Corney et al., 2010). Low variation between RCM simulations is also 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ff ff to ff . This is most ff replicates the range of cult to replicate as the ff ffi ers a closer match to ob- 30 %) are projected in the ff + o . Despite this, larger stream- ”, defined as gridded outputs ff ff 15 %). RCM simulations show ff > varied much more between RCM ff reasonably well. ff . In all catchments, SIMHYD SILO-runo ff overestimates larger observed streamflows are reasonably well replicated by SIMHYD ff 1798 1797 ff erences between medium observed streamflows and streamflows ff vary much more between RCM simulations than be- ff ff . Di tends to underestimate large (exceedance probabilities ff ff are largely caused by the hydrological models, and not by ff 15 %), but marked increases (up to − in several of the wetter catchments (Black River, Nive River, Huon River). projected with AWBM, Sacramento, SIMHYD and SMAR-G are very similar ff ff ects of the bias-corrected RCM inputs on hydrological performance are more ff Mean daily rainfall intensity is projected to increase over most of Tasmania (Fig. 9b). Mean annual APET is projected to increase across Tasmania, with the highest in- The e 10 %) observed streamflows (Black River, Nive River, Franklin River, Huon River), tween hydrological models. Fornual a runo given RCM simulation, future changes to mean an- 4.3 Projected changes in runo In describing projections wefrom distinguish the between hydrological “runo models,river and catchments. “streamflows”, calculated by aggregating runo 4.3.1 Variation between hydrological models andProjected RCMs changes to future runo intense events as theobservations (Allen climate and warms Ingram,at 2002; is least Pall a partly et consistent robust al., withStephens 2007; an feature and increase Petheram of Hu, in et atmospheric 2010). al., theory, moisture 2009) simulations (Hegerl and et and al., is 2004; creases in the western mountainschanges (Fig. in 9c). mean Increases annual in7 rainfall, APET %. with are All mean small RCM annual compared simulations to APET project increases Tasmania-wide increases always in less APET than The by largest 2070–2099. proportional increasesstrong occur agreement in the on east theTasmania ( by sign 2070–2099 of (Fig. 9b). change in The general mean tendency daily of rainfall rain intensity to fall for in much fewer, more of tainous centre (up to east. The increases in theannual east rainfall tend is to occur alsostrongly at projected on lower elevations. the along An sign the increaseelevations of in in south-west mean the change coast. mountainous in centre the (Fig. The lower-lying 9a). simulations parts agree of the east coast, and at high rainfall vary spatially. Reductions in mean annual rainfall are projected for the moun- Projected changes in rainfall and APETthe from mean 1961–1990 to of 2070–2099 the calculated from six RCM simulations are shown in Fig. 9. Changes in mean annual served streamflows than SIMHYD SILO-runo medium streamflows (exceedance probabilities ofcated 10–80 by %) are SIMHYD reasonably RCM-runo and well SIMHYD repli- RCM-runo the bias-corrected RCM inputs.observed Overall, streamflows and SIMHYD SIMHYD RCM-runo SILO-runo 4.2 Projected changes in rainfall and APET and this tendency is exacerbatedflows in generated SIMHYD RCM-runo by SIMHYDRCM-runo SILO-runo In catchments where SIMHYD SILO-runo (South Esk River, Clyde River), the SIMHYD RCM-runo for irrigation. easily seen in streamflowunderestimates duration larger curves streamflows (Fig. modelledprobably 8). with caused SIMHYD by In SILO-runo the general,storms inadequate by SIMHYD replication the RCM-runo of bias-corrected RCM the outputs,rainfalls temporal already SIMHYD characteristics described. SILO-runo of In catchments rain- < with high simulations in therivers) drier than in eastern the catchments wetter westernand (South and Huon southern River). Esk, catchments This (Black Little ismania. River, consistent Franklin Swanport River with The and the higher summer Clyde upper variability (DJF) reaches of of rainfall yields in this of eastern catchment the Tas- are impounded Clyde (Lake River Crescent/Sorell) are and regulated di bias-corrected RCM inputs. SIMHYD RCM-runo 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . ff ff ff ff runo 99 Q ) over most of 25 Q , while high runo , ff ff . ff events are larger than mean ff over Tasmania’s central moun- 60 % (Fig. 11a). ff (represented by 99th percentile > ff set by projected increases at lower runo by occur in the northern two-thirds of equates to a much greater increase ff ff ff 99 ff Q runo 99 . The RCM simulations agree strongly on a 1800 1799 ff Q is projected to increase in many areas of Tas- ff from IHACRES with bias-corrected RCM inputs. decreases more and over a wider area than decreases ff ff runo ect on annual streamflows in the Black and Franklin rivers as (here represented by 25th percentile runo 25 ff ff Q decreases markedly in the west (Fig. 11b), however these seasonal over much of Tasmania. The most marked increases in daily vari- ff ff (Fig. 11d). Increases in high runo events generally decrease more than mean runo ff ff makes a small contribution to streamflow in these rivers. Similar seasonal in many low-elevation areas in the east and in coastal areas (Fig. 11). The ) are more widespread and show similar proportional increases to mean ff ff 99 Q (Fig. 11e). The RCM simulations agree strongly on an increase in events, a proportional change in decreases by more than 30 % (Fig. 11a). In eastern Tasmania, rainfall increases , ff ff ff ff 20 % (Fig. 9a) are projected to increase runo < Variance in daily and annual runo Projections for rivers in the drier regions, including the north-east (South Esk River), A major feature of these projections is reduced runo Changes in seasonal streamflow projected with SIMHYD at the eight study catch- Low runo ance occur in the lowlands of the central east, which is consistent with an increase in Clyde River is projected totion experience areas year-round (not streamflow decreases shown), in butelevations, high these particularly eleva- decreases during are MAM, o resultingthe in catchment increased outlet. mean A annual similarthe elevation-sensitive streamflow streamflow South at response Esk is River observed in for the north-east. mania. Increases inTasmania (Fig. the 13), and variance the ofCV RCM simulations daily of agree daily runo strongly runo on projected increases in tains in all seasons (Fig.nual runo 11a–c). This contrastshigh-elevation with Nive River projected catchment increases is projected in toyear mean experience round, decreases an- particularly in in streamflow May andspan June (Fig. both 12). high Catchments and in central low Tasmania that elevations (e.g. Clyde River) show complex responses. The the central north of Tasmania isseasons, projected particularly to JJA experience (Fig. increases 12). in streamflows in all east (Little Swanport River)gree and centre of (Clyde variation River), betweenport are River characterised RCM are by simulations. projected a to highFebruary experience The to de- increases April. South in streamflow Esk (Fig. River 12), largely and during Little Swan- ments are shown inseason. Fig. DJF 12. runo Projecteddecreases changes have to little streamflows e varyDJF considerably runo by changes are also projected in the Huon River in the south-west. The Rubicon River in decrease in low runo Tasmania (Fig. 11d). to mean runo runo runo over the west coast, northruno and east. Because in streamflow than the same proportional change to mean runo In many areas, theWhere projected mean changes annual rainfall toruno in rainfall central are Tasmania amplifiedof decreases in by changes up to to runo 15 % (Fig.events 9a), increase similarly to mean runo and more widespread wetting thanIn other the hydrological downscaled models GFDL-CM2.1 for example,the all IHACRES east RCM projects and simulations. more stronger intensemodels. wetting wetting in in The the high westprojections sensitivity of and Tasmanian of south-west runo than IHACRES the to other changes hydrological in4.3.2 inputs renders Projections suspect from the the SIMHYD hydrological model Sacramento, SIMHYD and SMAR-Gchange (Fig. agree 10). strongly The onTasmania, four little the change hydrological in spatial models the show features south-west,SIMHYD drying and of wetting and in in runo central SMAR-G the and east. areof north-west AWBM, also low Sacramento, consistent and in high streamflows seasonal (not projections shown). and IHACRES consistently at projects projections more intense (Fig. 10). Using the downscaled GFDL-CM2.1 simulation as an example, AWBM, 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ff projected ff projections. ect the pro- to vary con- ff ff as realistically ff ff increases over most ar- ff erent and shorter training periods ff projections, even if the model has been ff vary far more between RCM simulations ff 1802 1801 ect on projected changes to mean annual rainfall (Bennett ff ective for climate studies elsewhere. ff erent projections of change. Viney et al. (2009a) found that IHACRES was ff Our fine-scale simulations project future changes to Tasmanian runo Projected changes in Tasmanian runo The IHACRES hydrological model does not replicate observed runo We note that the period chosen to train the quantile mapping may a Overall, the projections suggest that there will be a greater variability of streamflows, winter in the future, the air is warmer and moister as it approaches the west coast of finding supports several othersignificant studies source of that uncertainty have than shown(Prudhomme hydrological and climate models Davies, for models 2009; surface to water Teng et projections be al., a 2011; Wilby more andsiderably Harris, by 2006). region, in(Christensen contrast et to al., 2007). near-uniformfor spatial the Of central changes note mountains, projected areerate as by the Tasmania hydro-electric relies GCMs year-round power on decreases and streamflowsover in to the from runo supply central this mountains irrigators. region of to Tasmania The gen- reported projected here decrease has in seasonal dependence. runo For than between hydrological models. Thisperforming finding is IHACRES accentuated model if from weimportant exclude the to the projections. consider poorly the range For ofels our RCM simulations to study, than adequately therefore, the describe range it of uncertainty is hydrological mod- in more projections of surface water availability. This validation tests. Theyter attributed that this scales drop rainfall.particularly in sensitive performance In to contrast, to changesconditions the Vaze in for et IHACRES catchments inputs al. on parame- when continental (2010)bias-corrected calibrated Australia. RCM found to We variables that a conclude as that IHACRES rangeies, direct for was of it inputs studies not wet is to using important and hydrological toprecursor models dry to test for generating a impact stable, hydrological stud- plausible model runoshown for to sensitivity be to e changes in inputs as a our conclusions. as the other hydrologicalgives models di with bias-corrected RCMthe inputs. best Further, performed IHACRES model when calibrated, but performed worst under spatial cross- one RCM for our study. Replication of our method with another RCM would strengthen stantially with choice of trainingtile period mapping (Piani factors et and al., projections 2010b). could also Greater be variation in expected if quan- we had used more than quantile mapping factors areare not applied constant to when the di training projections period presented has in little thiset paper. e al., Crucially, 2011). however, varying Thisto the is the very high likely skill because ofhigh the the RCM corrections uncorrected skill applied RCM is are rainfall dueas usually simulations to the small (Corney the very due et bias-correction fine al., of horizontalmapping 2010). GCM resolution SSTs corrections of The before the are downscaling, outputs required, as used well in projected this changes study. If to larger rainfall quantile may vary more sub- Perturbation of historical datasetsniques based with on global pattern temperature changechanges scaling do in or not the have other number the or capacity simple sequence to scaling address of future rain tech- days. jections (Li et al., 2010). This was tested by Bennett et al. (2011), who showed that 5 Discussion and conclusions Our study demonstrates that quantiledrological mapping models can to directly produce couple realisticthe RCM streamflows. outputs high to Direct spatial hy- coupling and makesployed the in temporal most this resolution of study ofdrivers includes RCMs. of changes resulting rainfall. The from Whererainfall dynamical fundamental or the shifts downscaling the RCM in sequences em- projects in the changes which climate rain to falls, the these frequency are realised distributions in of the runo eas, with the most notable increases(Fig. projected 13). for the north-west and central highlands with rivers rising to higher peaks and experiencing longer periods of low streamflow. mean daily rainfall intensity (Fig. 9). Variance in annual runo 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | - ff er the ff ects on endemic ff is projected to decrease ff . Projected increases to inter-annual ff may not easily be captured by current erence in sign may be attributed to the ff ff ectively in future as they have in the past. ort has been expended in Australia in recent ff ff 1804 1803 in eastern Tasmania reported contrast with Post ff over western Tasmania are caused by a reduction in are an important feature of these projections. The pro- ff occur largely in the east in lowland areas, where water is ff ff variability could have as great an impact on Tasmanian water man- ff er periods of low inflows as e ff in eastern Tasmania by 2030. This di ff The implications for Tasmanian surface water availability and storage illustrate the Increased runo Changes in seasonal runo The projected increases in runo world. These implications cannotproach easily of be perturbing addressed historical throughchanged climate the in data the more future. that common A assumes ap- largeyears that amount building of complex rainfall e series variability of is hydrologicalfrom models un- to pattern assess scaling climate change of impacts al., GCMs 2009). (Charles et Ourusing al., paper high-resolution 2010; RCM has Chiew simulations shown when etof these that al., Australia. become there 2009; available for is Petheram other et the regions potential to update these studies age. In short, theinfrastructure. projected increases in runo virtue of using andrological ensemble models of to high-resolution understand the RCM nature projections of as future surface direct water inputs changes in to a hy- warmer agement practices as changes tovariability seasonal in runo streams fed bythat the the central large highlands hydropower andable and western to irrigation mountains bu storages could mean situatedProjected in increases in these runo areaspresently may stored not mostly be in smallprojected farm increases dams. in annual Small variability, dams even may if not there be is able more to water bu available on aver- including a poleward movement and strengtheningsure of the and subtropical an ridge increase of in high2001). pres- the Even high though phase reduced of DJFstreamflow the streamflows volumes, southern in these annular the changes mode westfreshwater are have (Kushner fish likely little et (Morrongiello to impact al., et on have al., annual deleterious 2011). e the Hadley cell and a poleward movement of the mid-latitude storm tracks (Yin, 2005), the mean westerly circulation (Grose et al., 2010), associated with an expansion of East Australian Current, andpressure the anomaly in formation the of(Grose Tasman a Sea et that small al., enhances but theshore 2010). significant onshore to mean the winds south sea The in and thisNotwithstanding level pattern east increased region due variability in to of the the streamflowsface (discussed coarser GCMs water below), grid increased availability is size sur- in of similar GCMs Tasmania’stural east but (Grose production. may et displaced present al., opportunities further 2011). for o future agricul- jected decreases in DJF runo et al. (2012), whose medianruno future scenario showed eitherincreased no resolution change or of decreases land-oceanprojections in used boundaries by in Post CCAM (2012).result in The from a increases comparison tendency in to for eastern increased the rainfall atmospheric projected GCM blocking, by southward CCAM extension of the and rainfall, along the westernmountains. slopes. In This addition, decrease withdries in decreased out rainfall clouds relative extends to and to the warmeris current the temperatures, available climate central locally the (see for APET surface evaporation. changes). Thusin As for the all a seasons, result, central runo less mountains moisture Tasmania’s relative central to mountains the will lower-lying reducecapacity areas. Tasmania’s (Bennett hydro-electric et Reduced power al., generation streamflows 2010). from increased upward motion alongthe the highest western elevations, slopes the ofsponse air the to descends, mountains. the and increased Aftercauses at a upward reaching a slight motion decrease greater further in ratethe rainfall west. decreased in in westerly the the airflow This projected central future in tendency plateau as the region. for future a In subsidence results the re- in other weaker upward seasons, motion, Tasmania. This causes an increase in rainfall along the west coast, which leads to 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , N. L.: ff doi:10.1175/1520- , 2009. across southeast Australia: ff ect of GCM bias on downscaled ff , 2002. , 2009. , 2007. , 2011. , N. L.: Climate Futures for Tasmania: water ff ). 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L. and Harris, I.: A framework for assessing uncertainties in climate change im- Zhang, X., Zwiers, F. W., Hegerl, G. C., Lambert, F. H., Gillett, N. P., Solomon, S., Stott, P. Viney, N. R., Post, D. A., Yang, A., Willis, M., Robinson, K. A., Bennett, J. C., Ling, F. L. N., and Viney, N. R., Perraud, J., Vaze, J., Chiew, F. H. S., Post, D. A., and Yang, A.: The useful- Vaze, J., Post, D. A., Chiew, F. H. S., Perraud, J.-M., Viney, N., and Teng, J.: Climate nonsta- Teng, J., Vaze, J., Chiew, F. H. S., Wang, B., and Perraud, J.-M.: Estimating theThatcher, relative uncer- M. and McGregor, J.: Using a scale-selective filter for dynamical downscal- 5 20 30 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ff Mean annual runo (c) Mean annual Morton’s (1983) wet APET cal- (b) 1814 1813 Mean annual rainfall. (a) Tasmanian historical climate (1961–2007) derived from the SILO climate dataset (Jef- Tasmania’s location (shaded) in relation to the Australian continent. generated with the SIMHYD model using SILO variables. culated from SILO temperature, solar radiation and vapour pressure. frey et al., 2001). Fig. 2. Fig. 1. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1816 1815 Catchments and streamflow gauges used to validate hydrological model performance. Catchments reported by this study. Fig. 4. Fig. 3. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) streamflows (bottom 5 . Q ff overestimate observations, and ff ). Points show the mean of the CV of daily streamflows gener- ff (b) overestimates SILO-forced runo ff CV of daily streamflows generated by SIMHYD 1818 1817 ) streamflows (middle panels) and low ( (a) 95 Q ) and observations (OBS). ff cients of variation (CV) of daily streamflows generated by SIMHYD ffi and SIMHYD forced with SILO (SILO-runo ff Non-exceedance probabilities of streamflow biases from the hydrological models forced with the RCM at Comparison of coe ated by RCM-runo at 86 streamflow gaugesforced for with the 1961–2007. RCM (RCM-runo six RCM simulations, bars show the range from the six simulations. Fig. 6. 86 streamflow gauges for 1961–2007. Leftshows column biases shows calculated biases calculated against against streamflowsare observed simulated streamflows, shown right with for mean column the streamflows hydrological (top models panels), forced high ( by SILO variables. Biases Fig. 5. panels). Lines show mean biasesfrom from the the six six RCM simulations. simulations,for For shaded right left confidence panels intervals panels positive show positive biases the biases mean range mean of that biases that RCM-forced runo RCM-forced runo Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1820 1819 Comparison of streamflow durations for observed and modelled daily streamflows Comparison of mean monthly modelled and gauged streamflows for 1961–2007. Blue Fig. 8. 1961–2007. Blue lineblue lines shows give range streamflows of modelled thered six line with RCM shows simulations, SIMHYD gauged black streamflows. line forced shows by SIMHYD forced the by SILO RCM, and faint Fig. 7. line shows streamflows modelled with SIMHYDthe forced six by RCM the RCM, simulations, faint black bluestreamflows. line lines shows give SIMHYD range forced of by SILO and red line shows gauged Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Change (c) Change in mean 1 mm. (a) > 1822 1821 from 1961–1990 to 2070–2099 for all RCM simulations ff Change in mean daily rainfall intensity for rain days (b) Change in mean annual runo Change in rainfall and APET from 1961–1990 to 2070–2099. and hydrological models. RCM simulationsand are are designated ordered by from the driest GCMs projectionHadCM3, used (CSIRO-Mk3.5, bottom top for panels) panels). downscaling, to wettest Hydrologicalpanels) projection models to (UKMO- are least biased ordered (SIMHYD, from right most panels). biased (IHACRES, left Fig. 10. in mean annual APET.Stippling All shows plots regions are where calculatedchange. at from least the five average of of the the six six RCM RCM simulations. simulations agree on the sign of annual rainfall. Fig. 9. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . ff Change (a) Change in mean JJA runo (c) . ff . Changes are calculated from the mean of ff runo 1824 1823 99 Q Change in Change in mean DJF runo simulated by SIMHYD from 1961–1990 to 2070–2099. (e) (b) ff . ff . ff runo 25 Q Change in runo Change in mean monthly streamflows simulated by SIMHYD from 1961–1990 to 2070– Change in 2099. Numbers in plots indicateRCM change simulations. in Numbers mean in annual streamflow brackets show from the the average range of of the change six from the six simulations. Fig. 12. (d) the six RCM simulations. Stipplingagree shows regions on where the at sign least of five of change. the six RCM simulations in mean annual runo Fig. 11. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . ff Changes to CV of annual runo simulated by SIMHYD from 1961– (b) ff . ff 1825 cient of variation (CV) of runo ffi Changes to CV of daily runo (a) Changes to coe Fig. 13. 1990 to 2070–2099. Changes are calculated from thewhere average at of least the five six of RCM the simulations. six Stippling RCM shows simulations regions agree on the sign of change.