A novel spatial downscaling approach for climate change assessment in regions with sparse ground data networks
This study proposes a novel approach that expands the existing QDM (quantile delta mapping) to address spatial bias, using Kriging within a Bayesian framework to assess the impact of using a point reference field. Our focus here is to spatially downscale daily rainfall sequences simulated by regiona...
| Main Authors: | Kim, Yong-Tak, Kwon, Hyun-Han, Lima, Carlos Henrique Ribeiro, Sharma, Ashish |
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| Format: | Artigo |
| Language: | Inglês |
| Published: |
Wiley
2021
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| Subjects: | |
| Online Access: |
https://repositorio.unb.br/handle/10482/42478 https://doi.org/10.1029/2021GL095729 |
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| Summary: |
This study proposes a novel approach that expands the existing QDM (quantile delta mapping) to address spatial bias, using Kriging within a Bayesian framework to assess the impact of using a point reference field. Our focus here is to spatially downscale daily rainfall sequences simulated by regional climate models (RCMs), coupled to the proposed QDM-spatial bias-correction, in which the distribution parameters are first interpolated onto a fine grid (rather than the observed daily rainfall). The proposed model is validated through a cross-validatory (CV) evaluation using rainfall data from a set of weather stations in South Korea and climate change scenarios simulated by three alternate RCMs. The results demonstrate the efficacy of the proposed model to simulate the bias-corrected daily rainfall sequences over large regions at fine resolutions. A discussion of the potential use of the proposed approach in the field of hydrometeorology is also offered. |
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