Statistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin
Climate change impact studies are generally carried out with higher resolution general circulation model (GCM) outputs, which are usually for a global scale, and it is difficult to use the same for a regional scale. GCM simulations require downscaling to get a coarser scale output for local climate...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IWA Publishing
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ce8bbdd7513d47ef8c92eac6536e588d |
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| Sumario: | Climate change impact studies are generally carried out with higher resolution general circulation model (GCM) outputs, which are usually for a global scale, and it is difficult to use the same for a regional scale. GCM simulations require downscaling to get a coarser scale output for local climate impact studies. In this study, an improvised principal component regression (PCR) downscaling technique is adapted to downscale 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM historical outputs. A massive river basin named Cauvery with 35 observation stations is categorized into three subbasins to study the regional climate impacts. In this case, the PCR model performed remarkably well compared to other conventional machine learning models with half the computational time than usual. The test statistics state that the validation of the proposed model illustrates a variance in calibration results of the PCR model, which ranges between 2 and 5%, and a variance in validation, which is less than 7% throughout the study area. Since it is desired to prioritize GCMs to choose the merely suitable models for a strategic climate study, the models were selected based on the PCR model performance. Furthermore, CCSM4, inmcm4, and EC-EARTH model's performance in recreating precipitation statistics over the study area are exceptional. HIGHLIGHTS
The evaluation and comparison of downscaling Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) based on renowned machine learning (ML) techniques.;
The suggestion of an alternate ML (principal component regression) approach for improvised downscaling.;
Subbasin-wise climate assessment on a large-scale river basin.;
The intercomparison of ML models with respect to calibration and validation periods.;
The outcome suggests an improvised climate downscaling approach with an appropriate CMIP5 GCM.; |
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