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Statistical Modeling of Precipitation Process for an Ungauged Site in the Context of Climate Change

Author(s): Myeong-Ho Yeo; Van-Thanh-Van Nguyen

Linked Author(s): Van-Thanh-Van Nguyen

Keywords: Precipitation; Regionalization; Statistical downscaling; Missing data; Climate change

Abstract: The overall objective of the present paper is to propose a statistical approach to downscaling of the precipitation process at an ungaged location in the context of climate change. More specifically, the proposed approach consists of a combination of three components: (i) a regionalization approach for identifying the homogeneous groups of observed daily precipitation series available at different raingages; (ii) a stochastic model for constructing daily rainfall events at an ungaged location within a homogeneous group; and (iii) a statistical downscaling model (SDRain) for describing the linkage between the constructed daily precipitation series and the large-scale climatic predictors given by GCM simulation outputs. The feasibility of the proposed stochastic approach has been assessed using the available daily precipitation data for the 1973-2001 period from a network of 62 raingage stations in South Korea and the NCEP reanalysis climate predictors. Results of the numerical application have indicated that it is feasible to estimate the missing precipitation data at an ungauged site based on the data available at other sites within the same homogeneous region. Furthermore, the proposed SDRain was able to generate daily precipitation sequences for an ungaged site with comparable statistical characteristics as those given by the application of SDRain for a gaged site with available observed precipitation data.


Year: 2014

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