
TY  - JOUR
A1  - Manage, Nadeeka Parana 
A1  - Lockart, Natalie 
A1  - Willgoose, Garry 
A1  - Kuczera, George 
A1  - Kiem, Anthony S. 
A1  - Chowdhury, AFM Kamal 
A1  - Zhang, Lanying 
A1  - Twomey, Callum 
T1  - Statistical testing of dynamically downscaled rainfall data for the Upper Hunter region, New South Wales, Australia
JF  - Journal of Southern Hemisphere Earth Systems Science
JO  - JSHESS
Y1  - 2016
VL  - 66
IS  - 2
SP  - 203
EP  - 227
KW  - 
UR  - https://doi.org/10.1071/ES16016 
N2  - This study tests the statistical properties of downscaled climate data, concentrating on the rainfall which is required for hydrology predictions used in water supply reservoir simulations. The datasets used in this study have been produced by the New South Wales (NSW) / Australian Capital Territory (ACT) Regional Climate Modelling (NARCliM) project which provides a dynamically downscaled climate dataset for southeast Australia at 10 km resolution. In this paper, we present an evaluation of the downscaled NARCliM National Centers for Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR) reanalysis simulations. The validation has been performed in the Goulburn River catchment in the Upper Hunter region of New South Wales, Australia. The analysis compared time series of the downscaled NARCliM rain-fall data with ground based measurements for selected Bureau of Meteorology rainfall stations and 5 km gridded data from the Australian Water Availability Project (AWAP). The initial testing of the rainfall was focused on autocorrelations as persistence is an important factor in hydrological and water availability analysis. Additionally, a cross-correlation analysis was performed at daily, fort-nightly, monthly and annually averaged time resolutions. The spatial variability of these statistics were calculated and plotted at the catchment scale. The auto-correlation analysis shows that the seasonal cycle in the NARCliM data is stronger than the seasonal cycle present in the ground based measurements and AWAP data. The cross-correlation analysis also shows a poor agreement between NARCliM data, and AWAP and ground based measurements. The spatial variability plots show a possible link between these discrepancies and orography at the catchment scale. 
ER  -

