HR: 09:30h
AN: GC31A-07    [Abstracts]
TI: A stochastic downscaling framework for multi-point daily rainfall for catchment scale climate change impact assessment
AU: Mehrotra, R
EM: raj@civeng.unsw.edu.au
AF: University of New South Wales, Civil and Environmental Engineering UNSW, Sydney, NSW 2052 Australia
AU: * Sharma, A
EM: a.sharma@unsw.edu.au
AF: University of New South Wales, Civil and Environmental Engineering UNSW, Sydney, NSW 2052 Australia
AB: Use of General Circulation Models (GCMs) for climate change impact assessment is often limited by their incapability at representing local features and dynamics at spatial scales finer than the in-built GCM grid scale. This has led to the development of downscaling techniques for transfer of coarse GCM simulated weather output to a finer scale. This paper presents a nonparametric stochastic downscaling framework for multi-site daily rainfall occurrence and amount. At site rainfall occurrences are downscaled using a nonparametric nonhomogeneous hidden Markov model (NNHMM) that represents spatial dependence across the rainfall occurrence field using a dynamic weather state indicative of the centroid and average wetness fraction of the rainfall occurrence field. The rainfall amounts on the wet days are downscaled using a non-parametric kernel density approach that accommodates variations in the rainfall downscaling model at individual locations while still representing the spatial dependence in the resulting field. Multi-site spatial correlations of rainfall amounts are represented by driving each of the single-site amounts model with spatially correlated random numbers. The developed downscaling model provides a better representation of spatial and temporal structure of the observed rainfall. The downscaling model is first calibrated using the relevant atmospheric variables and rainfall records of 30 stations around Sydney, Australia. Subsequently, the model is applied to predict plausible changes in rainfall using the same atmospheric variables corresponding to the future climate. The analyses of the results show that the logic of providing separate treatments for rainfall occurrence and amounts at individual locations imparts considerable accuracy in the representation of characteristics of interest in hydrologic studies. These characteristics include representation of rainfall spell patterns, spatial distribution of the rainfall occurrence and amount fields, representation of low and high rainfall extremes at individual stations and across the field, as well as generic indicators of water balance and variability that are of importance in a catchment scale water balance simulation. The developed downscaling model will be useful in catchment modelling and management and/or investigating possible changes that might be experienced by hydrological, agricultural, and ecological systems in future climates.
DE: 1616 Climate variability (1635, 3305, 3309, 4215, 4513)
DE: 1637 Regional climate change
DE: 1807 Climate impacts
DE: 3265 Stochastic processes (3235, 4468, 4475, 7857)
DE: 3305 Climate change and variability (1616, 1635, 3309, 4215, 4513)
SC: Global Climate Change [GC]
MN: Fall Meeting 2005