Título:	A novel spatial downscaling approach for climate change assessment in regions with sparse ground data networks
Autor(es):	Kim, Yong-Tak Kwon, Hyun-Han Lima, Carlos Henrique Ribeiro Sharma, Ashish
Assunto:	Mudanças climáticas Krigagem
Data de publicação:	6-Nov-2021
Editora:	Wiley
Referência:	KIM, Yong-Tak et al. A novel spatial downscaling approach for climate change assessment in regions with sparse ground data networks. Geophysical Research Letters, v. 48, n. 22, e2021GL095729, 2021. DOI: https://doi.org/10.1029/2021GL095729.
Abstract:	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.
Licença:	This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
DOI:	https://doi.org/10.1029/2021GL095729
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