Research Repository

Statistical downscaling of general circulation model outputs to precipitation accounting for non-stationarities in predictor-predictand relationships

Dhanapala Arachchige, Sachindra and Perera, B. J. C (2016) Statistical downscaling of general circulation model outputs to precipitation accounting for non-stationarities in predictor-predictand relationships. PLoS ONE, 11 (12). ISSN 1932-6203

[img] Text
file.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB)

Abstract

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data arcHIVe and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950-2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950-2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950-69, 1970-89 and 1990-99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP).

Item Type: Article
Uncontrolled Keywords: climate change; hydroclimatic; rainfall; genetic programming; downscaling models
Subjects: FOR Classification > 0905 Civil Engineering
FOR Classification > 0907 Environmental Engineering
Faculty/School/Research Centre/Department > College of Science and Engineering
Faculty/School/Research Centre/Department > Institute for Sustainability and Innovation (ISI)
Depositing User: Symplectic Elements
Date Deposited: 17 Sep 2017 23:53
Last Modified: 12 Apr 2019 03:05
URI: http://vuir.vu.edu.au/id/eprint/34514
DOI: https://doi.org/10.1371/journal.pone.0168701
ePrint Statistics: View download statistics for this item
Citations in Scopus: 12 - View on Scopus

Repository staff only

View Item View Item

Search Google Scholar