Estimation of rainfall elasticity of streamflow in Australia

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1 Hydrological Sciences Journal ISSN: -7 (Print) - (Online) Journal homepage: Estimation of rainfall elasticity of streamflow in Australia FRANCIS H. S. CHIEW To cite this article: FRANCIS H. S. CHIEW () Estimation of rainfall elasticity of streamflow in Australia, Hydrological Sciences Journal, :, -, DOI:./hysj... To link to this article: Published online: 9 Jan. Submit your article to this journal Article views: View related articles Citing articles: View citing articles Full Terms & Conditions of access and use can be found at

2 Hydrological Sciences Journal des Sciences Hydrologiques, () August Estimation of rainfall elasticity of streamflow in Australia FRANCIS H. S. CHIEW CSIRO Land and Water, GPO Box, Canberra, Australian Central Territories, Australia Also at: ewater CRC and The University of Melbourne Abstract Estimates of rainfall elasticity of streamflow in 9 catchments across Australia are presented. The rainfall elasticity of streamflow is defined here as the proportional change in mean annual streamflow divided by the proportional change in mean annual rainfall. The elasticity is therefore a simple estimate of the sensitivity of long-term streamflow to changes in long-term rainfall, and is particularly useful as an initial estimate of climate change impact in land and water resources projects. The rainfall elasticity of streamflow is estimated here using a hydrological modelling approach and a nonparametric estimator. The results indicate that the rainfall elasticity of streamflow (ε P ) in Australia is about.. (observed in about 7% of the catchments), that is, a % change in mean annual rainfall results in a..% change in mean annual streamflow. The rainfall elasticity of streamflow is strongly correlated to runoff coefficient and mean annual rainfall and streamflow, where streamflow is more sensitive to rainfall in drier catchments, and those with low runoff coefficients. There is a clear relationship between the ε P values estimated using the hydrological modelling approach and those estimated using the nonparametric estimator for the 9 catchments, although the values estimated by the hydrological modelling approach are, on average, slightly higher. The modelling approach is useful where a detailed study is required and where there are sufficient data to reliably develop and calibrate a hydrological model. The nonparametric estimator is useful where consistent estimates of the sensitivity of long-term streamflow to climate are required, because it is simple to use and estimates the elasticity directly from the historical data. The nonparametric method, being model independent, can also be easily applied in comparative studies to data sets from many catchments across large regions. Key words Australia; climate change; elasticity; hydrological modelling; nonparametric estimator; rainfall; sensitivity; streamflow Estimation de l élasticité de l écoulement en cours d eau par rapport à la pluie en Australie Résumé Des estimations de l élasticité de l écoulement en cours d eau par rapport à la pluie sont présentées pour 9 bassins Australiens. L élasticité de l écoulement par rapport à la pluie est définie ici comme étant le changement proportionnel de l écoulement annuel moyen divisé par le changement proportionnel de la pluie annuelle moyenne. L élasticité est donc une estimation simple de la sensibilité de l écoulement à long terme à des changements de la pluie à long terme, et est particulièrement utile comme première estimation de l impact du changement climatique dans des projets relatifs au territoire et aux ressources en eau. L élasticité de l écoulement par rapport à la pluie est estimée ici grâce à une approche de modélisation hydrologique et à un estimateur non-paramétrique. Les résultats indiquent que l élasticité de l écoulement par rapport à la pluie (ε P ) est d environ.. en Australie (observée pour environ 7% des bassins versants), ce qui signifie qu un changement de % dans la pluie annuelle moyenne engendre un changement de..% dans l écoulement annuel moyen. L élasticité de l écoulement par rapport à la pluie est fortement corrélée avec le coefficient d écoulement, et avec la pluie et l écoulement annuels moyens; l écoulement étant plus sensible à la pluie pour les bassins plus arides et pour ceux dont le coefficient d écoulement est faible. Il y a, pour les 9 bassins, une relation nette entre les valeurs de ε P estimées grâce à l approche de modélisation hydrologique et celles estimées grâce à l estimateur non-paramétrique, même si les premières sont en moyenne légèrement supérieures. L approche de modélisation est utile lorsqu une étude détaillée est requise et lorsque les données disponibles sont suffisantes pour développer et caler correctement un modèle hydrologique. L estimateur non-paramétrique est utile lorsque des estimations cohérentes de la sensibilité au climat de l écoulement à long terme sont requises, car il est simple d utilisation et estime l élasticité directement à partir des données historiques. La méthode non-paramétrique, indépendante de tout modèle, peut également être aisément appliquée à des jeux de données de nombreux bassins à travers de grandes régions pour des analyses comparatives. Mots clefs Australie; changement climatique; élasticité; modélisation hydrologique; estimateur non-paramétrique; pluie; sensibilité; écoulement en cours d eau Open for discussion until February 7

3 Francis H. S. Chiew INTRODUCTION There have been numerous studies on the sensitivity of streamflow to climate, in particular, to changes in rainfall and potential evapotranspiration. Many of these studies are carried out to estimate the potential impacts of climate change on streamflow and water resources. Most involve using a hydrological model, where (a) the model is calibrated against historical streamflow data; (b) the input climate data are modified to reflect an enhanced greenhouse environment; (c) the model is run using the modified input data and the same optimized parameter values; and (d) the simulated streamflow is compared against the historical streamflow to provide an estimate of the climate change impact on streamflow (e.g. Schaake, 99; Xu, 999; IPCC, ; Chiew & McMahon, ). The input data are almost always modified by scaling the historical rainfall and potential evapotranspiration time series by a constant factor, although some of the more recent studies also consider changes in the rainfall distribution (e.g. Charles et al., 999; Wang et al.; 999; Chiew et al., ). The modelling approach generally provides a reliable estimate of the sensitivity of streamflow to climate where a suitable model is used and calibrated properly. However, the choice of the model, calibration method and calibration criteria are subjective. There may also be a need to use different models for regions with different climatic and physical characteristics. The uncertainty in results arising from model choice can be overcome by estimating the sensitivity of streamflow to climate directly from the historical climate and streamflow data. The nonparametric estimator proposed by Sankarasubramaniam et al. () offers a potential for estimating the sensitivity of streamflow to rainfall directly from the historical data that is easily reproducible and defensible. The aim of this paper is to estimate the rainfall elasticity of streamflow in 9 catchments across Australia. The rainfall elasticity of streamflow (ε P ) is defined here as the proportional change in mean annual streamflow divided by the proportional change in mean annual rainfall (Schaake, 99; Sankarasubramaniam et al., ). An elasticity of. therefore indicates that a % change in rainfall results in a % change in streamflow. The main limitations of this approach are that it does not consider changes in the rainfall frequency and distribution, changes in vegetation characteristics under different climatic conditions, and potential feedbacks between the atmosphere and the land surface. However, the elasticity provides a simple estimate of the sensitivity of long-term streamflow to changes in long-term rainfall that can be used for assessing climate change impact in land and water resources projects, prior to, or in the absence of, a more detailed modelling study. Specifically, the paper (a) compares ε P estimated using two conceptual rainfall runoff models, (b) compares ε P estimated using the nonparametric estimator with ε P estimated using a rainfall runoff model, and (c) relates ε P to catchment hydroclimatic and physical characteristics. RAINFALL AND STREAMFLOW DATA The source of the data used for this study is the data set of daily rainfall time series, mean monthly areal potential evapotranspiration (APET) and monthly streamflow time

4 Estimation of rainfall elasticity of streamflow in Australia series for unimpaired catchments collated for an Australian Land and Water Resources Audit project (Peel et al., ). Unimpaired is defined here as data from unregulated rivers or where regulation changes the natural monthly streamflow volumes by less than %. The determination of whether the streamflow data are unimpaired is based on local knowledge and/or whether there is a significant dam upstream of the gauging station. The catchment areas range from to km. The source of the daily rainfall data is the Queensland Department of Natural Resources and Mines (about km km) interpolated gridded daily rainfall data for Australia from 89 to the present ( Jeffrey et al., ). The lumped catchment-averaged daily rainfall series is estimated from the daily rainfall in points within the catchment. The term rainfall, rather than precipitation, is used here because snowfall occurs in less than % of the catchments where it is only a small component of the annual precipitation. The mean monthly APET values are obtained from the Evapotranspiration Maps in the Climatic Atlas of Australia ( Australian Bureau of Meteorology, ). The APET values are derived using Morton s (98) wet environment ET algorithms. The use of mean monthly PET is sufficient for most rainfall runoff modelling applications, because the interannual variability of PET is relatively low and, compared to rainfall, the day-to-day variation in PET has little influence on the water balance at a monthly time scale. The monthly streamflow data are obtained from the respective state water agencies in Australia. Data from 9 catchments (out of the catchments that have at least years of data) are used for this study. Figure shows the catchment locations. The length of data ranges from to 9 years (th percentile, median and 9th percentile of, and years respectively), mean annual rainfall ranges from to 9 mm (, 97, ) and the runoff coefficient ranges from to.8 (8,.9, ). The Solid squares show the catchments used in both the SIMHYD and AWBM modelling studies. Fig. Locations of catchments used for this study.

5 Francis H. S. Chiew catchments cover a large range of hydroclimatic and physical characteristics, are located in the more populated and important agricultural regions, and generally reflect the availability of long, unimpaired streamflow data in Australia. As for all analyses, the results are very dependent on the quality of data used. The data set used here is relatively good and is the best available large rainfall and streamflow data set for Australia. ELASTICITY ESTIMATION USING TWO RAINFALL RUNOFF MODELS Two of the most extensively tested and used rainfall runoff models in Australia are used here SIMHYD (Chiew et al., ) and AWBM (Boughton & Chiew, ). Both are simple lumped conceptual daily rainfall runoff models that estimate daily runoff using daily rainfall and PET as input data. The model structures of SIMHYD and AWBM are shown in Figs and, respectively. The SIMHYD model has seven parameters and the version of AWBM used here has three parameters (model parameters are highlighted in bold italics in Figs and ). The SIMHYD model is applied to all 9 catchments, with the model parameters optimized to provide a good fit between the modelled and recorded monthly runoffs (by minimizing the sum of squares of the difference between the modelled and recorded monthly runoffs). Figure shows the distribution of Nash-Sutcliffe efficiency (Nash & Sutcliffe, 97) values summarizing the agreement between the modelled and recorded monthly runoffs in the 9 catchments [E =. indicates that all the modelled monthly runoffs are the same as the recorded monthly runoffs, E >. generally indicates satisfactory model calibration (Chiew & McMahon, 99)]. Figure indicates that SIMHYD has generally been calibrated successfully in most of PET RAIN excess C C C A A A recharge = BFI x excess surface runoff = ( - BFI) x excess GW baseflow = K b x GW In UGAWBM (used for this study), A =., A =., A =. C = 7 C ave, C =.7 C ave, C =. C ave Fig. Model structure of AWBM (the three model parameters are highlighted in bold italics).

6 Estimation of rainfall elasticity of streamflow in Australia 7 IMAX RAIN PET groundwater recharge interception store INR GW groundwater store REC RMO F saturation excess runoff and interflow (SRUN) S ET SMF SMSC SMS soil moisture store baseflow (BAS) infiltration excess runoff (IRUN) PET = areal potential evapotranspiration (input data) RUNOFF IMAX = lesser of { INSC, PET } INT = lesser of { IMAX, RAIN} INR = RAIN - INT RMO = lesser of { COEFF exp (-SQ x SMS/SMSC), INR } IRUN = INR - RMO SRUN = SUB x SMS/SMSC x RMO REC = CRAK x SMS/SMSC x (RMO - SRUN) SMF = RMO - SRUN - REC POT = PET - INT ET = lesser of { x SMS/SMSC, POT } BAS = K x GW Model parameters INSC interception store capacity (mm) COEFF maximum infiltration loss (mm) SQ infiltration loss exponent SMSC soil moisture store capacity (mm) SUB constant of proportionality in interflow equation CRAK constant of proportionality in groundwater recharge equation K baseflow linear recession parameter Fig. Model structure of SIMHYD (the seven model parameters are highlighted in bold italics). the catchments, with E values greater than.8 obtained in more than % of the catchments, greater than.7 in more than 8% of the catchments, and less than. in only ten catchments. The AWBM model has also been applied to the 9 catchments, but the ε P values estimated by SIMHYD and AWBM are compared here only for catchments (see Fig. ). The E values from the calibration of both models are greater than.8 in these catchments. The sensitivity of the total streamflow estimated by SIMHYD and AWBM to the input data is evaluated by: changing the input data; running the model with the

7 8 Francis H. S. Chiew. Nash-Sutcliffe efficiency (E) Proportion of catchment where E value is exceeded Fig. Distribution of Nash-Sutcliffe E values from the calibration of SIMHYD in the 9 catchments. optimized parameter values; and then comparing the modelled streamflow for the changed input data with the modelled streamflow using the original data (using data from 9 to 998). For the sensitivity analysis, the daily rainfall series is scaled by %, %, % and +% and the mean monthly potential evapotranspiration (PET) is scaled by %, +% and +%. The sensitivity of the modelled streamflow to changes in rainfall and PET is expressed as: δq = ε P δp + ε PET δpet () where δq is change in modelled mean annual streamflow, δp is change in mean annual rainfall and δpet is change in mean annual PET. The rainfall elasticity of streamflow (ε P ) and the PET elasticity of streamflow (ε PET ) are determined by method of least squares, by minimizing the sum of squares of the difference between δq determined from the SIMHYD and AWBM modelling results and δq estimated by equation (). The changes in streamflow (δq) determined from the modelling results and estimated using equation () are very similar for the range of rainfall and PET changes considered here (standard error of about % of mean annual streamflow). The elasticity estimates are also not dependent on the catchment area ( km ), length of data (greater than years) or the quality of model calibration (both models are satisfactorily calibrated in all catchments). Figure compares the values of ε P and ε PET estimated using the SIMHYD and AWBM models. The ε P values from the SIMHYD and AWBM simulations are very similar, with an average value of. and standard deviation of. across the catchments, and a correlation R of.8 and Nash-Sutcliffe E of. between the ε P values from SIMHYD and AWBM. [R is the correlation of the linear regression between two variables (measures the closeness of the data points to the line of best fit) and E measures the agreement between the two variables (closeness of the data points to the : line).] As both models have been extensively tested in Australia and satisfactorily calibrated to data from these catchments, the results suggest that the use

8 Estimation of rainfall elasticity of streamflow in Australia 9 εp from AWBM modelling ε P from SIMHYD modelling εpet from AWBM modelling ε PET from SIMHYD modelling Fig. Comparison of ε P and ε PET values estimated for catchments from SIMHYD and AWBM modelling. of appropriate and properly calibrated hydrological models is likely to give similar and reliable estimates of ε P. The ε P values estimated using different hydrological models that are properly calibrated and applied could be expected to be similar because streamflow responds directly to rainfall. However, the ε PET values estimated using different hydrological models are more likely to be different because of the different methods used by different models to simulate evapotranspiration (from soil wetness and PET). For example, although there is a significant correlation between the ε PET values from the SIMHYD and AWBM simulations (R =.9, see Fig. ), the modelled streamflow in the AWBM is considerably more sensitive to PET, with higher absolute ε PET values in the AWBM compared to SIMHYD (E <, average ε PET value of about. in SIMHYD and.8 in AWBM, see Fig. ). ELASTICITY ESTIMATION USING A NONPARAMETRIC ESTIMATOR AND THE RAINFALL RUNOFF MODEL The nonparametric estimator proposed by Sankarasubramaniam et al. () is used here to estimate ε P for the 9 catchments. The nonparametric estimator can be expressed as: Qt ε P = median ( P t Q P P ) () Q where P and Q are the mean annual rainfall and streamflow respectively. To estimate Qt Q ε P, a value of ( P ) is calculated for each pair of Pt and Qt in the annual time Pt P Q series, and the median of these values is the nonparametric estimate of ε P. This nonparametric estimator of ε P is therefore defined at the mean value of the hydroclimatic variable. Sankarasubramaniam et al. () carried out Monte Carlo experi-

9 Francis H. S. Chiew εp (Nonparametric) ε P (SIMHYD) Fig. Comparison of ε P values estimated for 9 catchments using SIMHYD and a nonparametric estimator. ments to compare the nonparametric estimator with ε P estimated from hydrological modelling approaches and concluded that this nonparametric estimator has low bias and is as robust as, or more robust than, ε P estimated using modelling approaches. The scatter plot in Fig. compares the ε P values estimated by the nonparametric estimator with values estimated from the SIMHYD modelling for the 9 catchments. The plot shows a clear relationship between the ε P values estimated using the two methods (R =.), but only a reasonable agreement between them (E = ). The average ε P value from the SIMHYD modelling for the 9 catchments is., with ε P values between. and. in more than 7% of the catchments. The average ε P value estimated by the nonparametric estimator is.8, with ε P values between. and. in about 7% of the catchments, and ε P values between. and. in about % of the catchments. The difference between the two estimates of ε P, defined by: DIFF = ε P ( SIMHYD) ε ε P P ( Nonparametric) ( SIMHYD) is compared against various catchment hydroclimatic and modelling characteristics in Fig. 7. A positive value of DIFF indicates that ε P estimated by SIMHYD is higher than ε P estimated using the nonparametric estimator, and vice versa. With 9 catchments used in the analyses, a correlation of. is statistically significant at α =. Figure 7 indicates that there is little or no relationship between DIFF and the various characteristics. There is no relationship between DIFF and the mean annual rainfall and streamflow (Fig. 7(a) and (b)). There is a very small, but statistically significant (at α < ) negative correlation between DIFF and the runoff coefficient (mean annual streamflow divided by mean annual rainfall), with Fig. 7(c) suggesting that ε P estimated by SIMHYD tends to be larger than ε P estimated by the nonparametric estimator in catchments with low runoff coefficients. A possible explanation for this is the highly nonlinear rainfall runoff relationship in ephemeral catchments with low runoff coefficients (little runoff in most years with occasional

10 Estimation of rainfall elasticity of streamflow in Australia.. (a).. (e) R <. R < Mean annual rainfall (mm) Annual streamflow skew.. (b) (f) DIFF R <. R < Mean annual streamflow (mm) Annual streamflow serial correlation. (c). (g) R < Runoff coefficient. (d) R < Length of data (years). (h) R < Humidity index -. R < E for SIMHYD calibration Fig. 7 DIFF (difference between ε P estimated by SIMHYD and ε P estimated by nonparametric estimator) vs catchment hydroclimatic and modelling characteristics. Qt Q high runoff in some years), and by considering only the median value of ( P ), Pt P Q the nonparametric estimator does not directly take into account the occasional high runoff values. Figure 7(d) also shows a very small, but statistically significant correlation between DIFF and the humidity index (mean annual rainfall divided by mean annual PET), where ε P estimated by SIMHYD is almost always higher than that estimated by

11 Francis H. S. Chiew the nonparametric estimator in catchments with humidity index greater than. (discussed further in the next section). Figure 7(e) shows a very small negative correlation between DIFF and the skew in annual streamflow. It is difficult to interpret this relationship because DIFF is negatively correlated with the runoff coefficient and positively correlated with the humidity index, whilst the skew is negatively correlated to both the runoff coefficient and humidity index (with stronger correlations than the DIFF vs runoff coefficient and DIFF vs humidity index correlations). Figure 7(f) indicates that there is no relationship between DIFF and the serial correlation in annual streamflow, despite the possibility that by taking into account catchment storage, the modelling approach may give a more reliable estimate of ε P than the nonparametric estimator in catchments with high streamflow serial correlation. Figure 7(g) also shows no correlation between DIFF and the length of data, suggesting that where there are at least years of data, the use of more data does not considerably improve the nonparametric estimate of ε P. Figure 7(h) shows a small correlation between DIFF and the E values for the SIMHYD calibration, where ε P estimated by SIMHYD is almost always higher than that estimated by the nonparametric estimator in the poorly calibrated catchments (E <.). However, the poor calibrations generally occur in catchments with low runoff coefficients, where ε P estimated by SIMHYD is also generally higher than that estimated by the nonparametric estimator. ELASTICITY AND CATCHMENT CHARACTERISTICS Figure 8 shows ε P values plotted against various catchment hydroclimatic and physical characteristics and the maps in Fig. 9 show categories of ε P values estimated from the SIMHYD modelling and by the nonparametric estimator for the 9 catchments. The main purpose of Fig. 9 is to show how ε P varies spatially across Australia, rather than to compare ε P values estimated by SIMHYD and the nonparametric estimator. There is a strong negative correlation between ε P and runoff coefficient, particularly ε P estimated from the SIMHYD modelling (Fig. 8(a)), indicating that streamflow is more sensitive to rainfall in catchments with low runoff coefficients. This is because of the nonlinearity in the rainfall runoff process, and the same absolute change in streamflow for a given absolute change in rainfall would be reflected as a higher ε P in a catchment with low runoff coefficient. In most situations, the upper limit of ε P is the inverse of runoff coefficient (when the absolute change in streamflow is the same as the absolute change in precipitation). The ε P vs runoff coefficient relationship is also reflected in the correlations between ε P and rainfall (Fig. 8(b)) and between ε P and streamflow (not shown here), but both the ε P vs rainfall and ε P vs streamflow correlations are weaker than the correlation between ε P and runoff coefficient. Figure 9 also shows that the coastal catchments in eastern Australia have lower ε P than the catchments in southern Australia and southwestern Australia (where runoff coefficients are lower than in the coastal catchments in eastern Australia), and catchments further inland (where rainfall and runoff coefficients are lower than in the coastal catchments in eastern Australia).

12 Estimation of rainfall elasticity of streamflow in Australia ε P (SIMHYD) ε P (Nonparametric estimator) (a) Runoff coefficient Runoff coefficient (b) Mean annual rainfall (mm) Mean annual rainfall (mm) (c) Humidity index Humidity index (d) 8 8 Percentage woody vegetation Percentage woody vegetation (e) Soil plant water holding capacity (mm) Soil plant water holding capacity (mm) Fig. 8 ε P vs catchment characteristics.

13 Francis H. S. Chiew SIMHYD Nonparametric estimator (a) + <.. -. >. (b) Fig. 9 Estimated ε P values in the 9 catchments: (a) using SIMHYD and (b) using a nonparametric estimator. There is also a negative correlation between ε P and humidity index (mean annual rainfall divided by mean annual PET) (Fig. 8(c)). The strength of the ε P vs humidity index relationship is similar to that of ε P vs rainfall, as there is no significant correlation between ε P and PET. Figure 8(c) also indicates that the ε P values estimated by SIMHYD are generally higher than those obtained by the nonparametric estimator in regions with high humidity index. In addition, unlike the nonparametric estimator, there also appears to be a low limit of about. in the ε P estimated by SIMHYD. The relationship between ε P and various catchment characteristics, based on the soils, vegetation and land use data sets that cover whole of Australia ( is also investigated here. The strongest relationships are observed between ε P and percentage woody vegetation (Fig. 8(d)) and between ε P and soil plant water holding capacity (Fig. 8(e)) (both correlations are much weaker than the correlation between ε P and rainfall). The small negative correlations suggest that streamflow is more sensitive to rainfall where there is smaller effective catchment storage (less vegetation or smaller effective soil store). CONCLUSIONS This paper presents estimates of rainfall elasticity of streamflow in 9 catchments across Australia, estimated using a hydrological modelling approach and a nonparametric estimator. The results indicate that the rainfall elasticity of streamflow (ε P ) is about.. (observed in about 7% of the catchments), that is, a % change in mean annual rainfall results in a..% change in mean annual streamflow. The rainfall elasticity of streamflow is very strongly correlated to runoff coefficient, where streamflow is more sensitive to rainfall in catchments with low runoff coefficients. The elasticity is also strongly correlated to mean annual rainfall and streamflow, with streamflow being more sensitive to rainfall in drier catchments. The modelling results from two rainfall runoff models suggest that the rainfall elasticity of streamflow estimated using different hydrological models should be similar because streamflow responds directly to rainfall. However, the sensitivity of

14 Estimation of rainfall elasticity of streamflow in Australia streamflow to other climatic variables (such as PET) estimated using different hydrological models is likely to be different because of the different methods used by different models to simulate evapotranspiration and other processes. The results indicate that there is a clear relationship between the ε P values estimated using the SIMHYD model and the nonparametric estimator for the 9 catchments, although the values estimated by SIMHYD are on average slightly higher. The modelling approach is useful where a detailed study is required and where there are sufficient data to reliably develop and calibrate a hydrological model. The nonparametric estimator is useful where consistent estimates of the sensitivity of longterm streamflow to climate are required, because it is simple and estimates the elasticity directly from the historical data. The nonparametric method, being model independent, can also be easily applied to data sets from many catchments across large regions in comparative studies. REFERENCES Australian Bureau of Meteorology () Climatic Atlas of Australia: Evapotranspiration. Australian Bureau of Meteorology, Melbourne, Australia. Boughton, W. C. & Chiew, F. H. S. () Calibrations of the AWBM for use on ungauged catchments. Technical Report /, Cooperative Research Centre for Catchment Hydrology, Melbourne, Australia. Charles, S. P., Bates, B. C. & Hughes, J. P. (999) A spatiotemporal model for downscaling precipitation occurrence and amounts. J. Geophys. Res., 7 9. Chiew, F. H. S. & McMahon, T. A. (99) Assessing the adequacy of catchment streamflow yield estimates. Aust. J. Soil Res., 8. Chiew, F. H. S. & McMahon, T. A. () Modelling the impacts of climate change on Australian streamflow. Hydrol. Processes,. Chiew, F. H. S., Harrold, T. I., Siriwardena, L., Jones, R. N. & Srikanthan, R. () Simulation of climate change impact on runoff using rainfall scenarios that consider daily patterns of change in GCMs. In: Proc. Int. Congress on Modelling and Simulation (MODSIM ) (Townsville, July ), vol., 9. Modelling and Simulation Society of Australia and New Zealand. Chiew, F. H. S., Peel, M. C. & Western, A. W. () Application and testing of the simple rainfall runoff model SIMHYD. In: Mathematical Models of Small Watershed Hydrology and Applications (ed. by V. P. Singh & D. K. Frevert), 7. Water Resources Publications, Littleton, Colorado, USA. IPCC (Intergovernmental Panel on Climate Change) () Climate Change : Impacts, Adaptation and Vulnerability Contribution of Working Group II to the Third Assessment Report. Cambridge University Press, Cambridge, UK. Jeffrey, S. J., Carter, J. O., Moodie, K. M. & Beswick, A. R. () Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Modell. Software, 9. Morton, F. I. (98) Operational estimates of areal evapotranspiration and their significance to the science and practice of hydrology. J. Hydrol., 7. Nash, J. E. & Sutcliffe, J. V. (97) River forecasting using conceptual models,. A discussion of principles. J. Hydrol., 8 9. Peel, M. C., Chiew, F. H. S., Western, A. W. & McMahon, T. A. () Extension of unimpaired monthly streamflow data and regionalisation of parameter values to estimate streamflow in ungauged catchments. Report prepared for the Australian National Land and Water Resources Audit ( Sankarasubramaniam, A., Vogel, R. M. & Limburner, J. F. () Climate elasticity of streamflow in the United States. Water Resour. Res. 7, Schaake, J. C. (99) From climate to flow. In: Climate Change and U.S. Water Resources (ed. by P. E. Waggoner), Ch. 8, 77. John Wiley & Sons Inc., New York, USA. Wang, Q. J., Nathan, R. J., Moran, R. J. & James, B. (999) Impact of climate change on the security of water supply of the Campaspe system. In: Proc. Water 99 Joint Congress (Brisbane, July 999),. Engineers Australia, Canberra, Australia. Xu, C. Y. (999) Climate change and hydrologic models: a review of existing gaps and recent research developments. Water Resour. Manage., 9 8. Received September ; accepted March