Abstract
In this paper, downscaling models are developed using Partial Least Squares (PLS) Regression for obtaining projections of mean monthly precipitation to lake-basin scale in an arid region in India. The effectiveness of this approach is demonstrated 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. In this paper, we use PLS regression for quality prediction and its use for the variable selection based on the variable importance. The results of downscaling models using PLS regression show that precipitation is projected to increase in future for A2 and A1B scenarios, whereas it is least for B1 and COMMIT scenarios using predictors.
Highlights
A general circulation model is a numerical mathematical model that gives the analysis of atmosphere in all three spatial dimensions based on conservation laws of momentum, energy and water vapor
The results of downscaling models using Partial Least Squares (PLS) regression show that precipitation is projected to increase in future for A2 and A1B scenarios, whereas it is least for B1 and COMMIT scenarios using predictors
We present a downscaling methodology based on partial least square (PLS) regression technique to study climate change impact over Pichola lake basin in an arid region
Summary
A general circulation model is a numerical mathematical model that gives the analysis of atmosphere in all three spatial dimensions based on conservation laws of momentum, energy and water vapor These models are the most reliable tool for estimating the changes in the climate. The methods used to convert GCM outputs into local meteorological variables required for reliable hydrological modeling are usually referred to as “downscaling” techniques: [3,4] Hydrologic variables, such as precipitation, etc., are significant parameters for climate change impact studies. The PLS approach is useful when one or a set of dependent variables (or time series) need to be predicted by a large set of predictor variables (or time series) that are strongly cross-correlated. For details of PLS regression, one can refer to Manne [12] and Wold [13]
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