Parameter uncertainty of the AWBM model when applied to an ungauged catchment Md Mahmudul Haque, 1 Ataur Rahman, 1 * Dharma Hagare 1 and Golam Kibria 2 1 School of Computing, Engineering and Mathematics, University of Western Sydney, Penrith, NSW 2751, Australia 2 Sydney Catchment Authority, Penrith, NSW 2750, Australia Abstract: In this study, a quantitative assessment of uncertainty was made in connection with the calibration of Australian Water Balance Model (AWBM) for both gauged and ungauged catchment cases. For the gauged catchment, five different rainfall data sets, 23 different calibration data lengths and eight different optimization techniques were adopted. For the ungauged catchment case, the optimum parameter sets obtained from the nearest gauged catchment were transposed to the ungauged catchments, and two regional prediction equations were used to estimate runoff. Uncertainties were ascertained by comparing the observed and modelled runoffs by the AWBM on the basis of different combinations of methods, model parameters and input data. The main finding from this study was that the uncertainties in the AWBM modelling outputs could vary from 1.3% to 70% owing to different input rainfall data, 5.7% to 11% owing to different calibration data lengths and 6% to 0.2% owing to different optimization techniques adopted in the calibration of the AWBM. The performance of the AWBM model was found to be dominated mainly by the selection of appropriate rainfall data followed by the selection of an appropriate calibration data length and optimization algorithm. Use of relatively short data length (e.g. 3 to 6 years) in the calibration was found to generate relatively poor results. Effects of different optimization techniques on the calibration were found to be minimal. The uncertainties reported here in relation to the calibration and runoff estimation by the AWBM model are relevant to the selected study catchments, which are likely to differ for other catchments. The methodology presented in this paper can be applied to other catchments in Australia and other countries using AWBM and similar rainfall–runoff models. Copyright © 2014 John Wiley & Sons, Ltd. KEY WORDS model calibration; water balance; rainfall–runoff; model uncertainty; AWBM Received 4 March 2014; Accepted 30 June 2014 INTRODUCTION Rainfall–runoff modelling plays an important role in many areas of hydrology including estimation of design floods, analysis of catchment yield and evaluation of the impacts of land use changes on water resources. Rainfall– runoff models are also used in assessing climate change impacts on water resources (Yilmaz et al., 2011; Islam et al., 2013). A rainfall–runoff model needs to be calibrated and validated using the observed climatic and runoff data; however, in ungauged catchments, the calibration and validation cannot be undertaken directly owing to unavailability of some or all of these observed data. Researchers in many countries attempted to develop rainfall–runoff models for ungauged catchments but with limited success (Boughton, 2009). A number of initiatives including Prediction in Ungauged Basins (Sivapalan et al., 2003) and the Model Parameter Estimation Experiment (Duan et al., 2006) coordinated multi- national efforts to enhance the accuracy of runoff prediction in ungauged catchments. Generally, regional relationships are used to estimate the parameters of a rainfall–runoff model for application in an ungauged catchment. Mainly two regionalisation principles are reported in the scientific literature for this purpose: (i) calibrate the hydrological model in the nearby gauged catchments and transpose the model parameters to the ungauged catchment; and (ii) derive relationship between the model parameters and catchment attributes on the basis of gauged catchments and use these relationship to predict model parameters at the ungauged catchment (Merz et al., 2006). Estimation of runoff with a reasonable accuracy in an ungauged catchment is regarded as a challenging task as notable uncertainties are involved in the regionalisation technique (Sivapalan, 2003; Goswami et al., 2007). In order to transpose the *Correspondence to: Ataur Rahman, School of Computing, Engineering and Mathematics, University of Western Sydney, Building XB, Room 2.48, Kingswood, Penrith Campus, Locked Bag 1797, Penrith, NSW 2751, Australia. E-mail: a.rahman@uws.edu.au HYDROLOGICAL PROCESSES Hydrol. Process. (2014) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/hyp.10283 Copyright © 2014 John Wiley & Sons, Ltd.