Abstract
The overall objective of the present paper is to propose a statistical approach to downscale the precipitation process at an ungauged 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 raingauges; (ii) a stochastic model for constructing daily rainfall events at an ungauged 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 the GCM simulation outputs. The feasibility of the proposed stochastic approach has been assessed using the available daily precipitation data for the period 1973–2001 from a network of 63 raingauge 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 ungauged site with comparable statistical characteristics as those given by the application of SDRain for a gauged site with available observed precipitation data.
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Yeo, MH., Nguyen, VTV. (2016). A Statistical Approach to Downscaling of Daily Rainfall Process at an Ungauged Site. In: Gourbesville, P., Cunge, J., Caignaert, G. (eds) Advances in Hydroinformatics. Springer Water. Springer, Singapore. https://doi.org/10.1007/978-981-287-615-7_20
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DOI: https://doi.org/10.1007/978-981-287-615-7_20
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