Abstract
Non-stationarity is an intrinsic property of all natural processes, and addressing the same is crucial for climatic downscaling models. Wavelet-based models have been used to address the non-stationarity in the individual predictor (explanatory) time series where each predictor is “decomposed” into its discrete wavelet components at multiple time–frequency resolutions. However, in the warming climate, the predictor–predictand relationships (PPRs) are getting unpredictable. Hence, it is important to understand if the wavelet-based approach can capture the non-stationary PPR better than the stand-alone models. This paper provides an experimental approach to compare the strength of wavelet-based and stand-alone regression models when applied to downscale mean monthly temperature, from general circulation models (GCMs). For this study, we use Can CM4 GCM model to downscale temperature at multiple locations in the Krishna River Basin. Regression coefficients of the recursively updated models are compared for the wavelet-based and the stand-alone models for the length of the validation period. The comparison shows that the regression coefficients from the wavelet-based models capture higher variance compared to the stand-alone models and hence were able to capture the changing PPRs in the downscaling models with greater accuracy. The statistical performance indices reinforce the finding that wavelet-based models consistently outperformed the stand-alone models.
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Sehgal, V., Sridhar, V., Rathinasamy, M. (2019). Comparative Analysis of the Performance of Wavelet-Based and Stand-alone Models in Capturing Non-stationarity in Climate Downscaling. In: Rathinasamy, M., Chandramouli, S., Phanindra, K., Mahesh, U. (eds) Water Resources and Environmental Engineering II. Springer, Singapore. https://doi.org/10.1007/978-981-13-2038-5_18
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