Abstract
In this paper, downscaling models are developed using various linear regression approaches namely direct, forward, backward and stepwise regression for downscaling of GCM output to predict mean monthly precipitation under IPCC SRES scenarios to watershed-basin scale in an arid region in India. The effectiveness of these regression approaches is evaluated through application to downscale the predictand for the Pichola lake region in Rajasthan state in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from (1) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000, and (2) the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1 and COMMIT for the period 2001–2100. The selection of important predictor variables becomes a crucial issue for developing downscaling models since reanalysis data are based on wide range of meteorological measurements and observations. Direct regression was found to yield better performance among all other regression techniques explored in the present study. The results of downscaling models using both approaches show that precipitation is likely to increase in future for A1B, A2 and B1 scenarios, whereas no trend is discerned with the COMMIT.
Highlights
Global circulation models (GCMs) are important tool in assessment of climate change
We explore four linear regression approaches; namely, (a) direct regression, (b) forward regression, (c) backward regression and (d) stepwise regression as a downscaling methodology to study climate change impact over Pichola lake basin in an arid region
This paper investigates the applicability of the various linear regression approaches such as direct, forward, backward and stepwise to downscale precipitation from
Summary
Global circulation models (GCMs) are important tool in assessment of climate change. These are numerical models that have been designed to simulate the past, present, and future climate [1]. These models remain relatively coarse in resolution and are unable to resolve significant subgrid scale features. In most climate change impact studies, such as hydrological impacts of climate change, impact models are usually required to simulate sub-grid scale phenomenon and require input data at similar sub-grid scale. The methods used to convert GCM outputs into local meteorological variables required for reliable hydrological modeling are usually referred to as “downscaling” techniques [2,3]. A proper assessment of probable future precipitation and its variability is to be made for various water resources planning and hydro-climatology scenarios
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