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Analysis of the Kernel Bandwidth Influence in the Double Smoothing Merging Algorithm to Improve Rainfall Fields in Poorly Gauged Basins

Author(s): Nicolas Duque-Gardeazabal; David Zamora; Erasmo Rodriguez

Linked Author(s): Erasmo Alfredo Rodriguez Sandoval, Nicolas Duque Gardeazabal

Keywords: Kernel Smoothing; Merging; MSWEP; Network configuration; Rain gauge density

Abstract: Accurate estimates of precipitation are needed for many applications in hydrology as rainfall is one of the most influential variables of the water cycle. The common sources of information used to estimate rainfall fields are in situ rain gauges, remote sensing information and outputs from climate models. However, each of the above-mentioned sources has its own limitations, which can be reduced by blending information from these sources, in a product that takes advantage of the strengths of each dataset. In this research we study the double smoothing merging algorithm, creating a rainfall distributed product that combines remote sensed and reanalysis data, and information from a rain gauge network. The main objective of the study is to investigate the implications of varying the rain gauge density and configuration, on the merging parameters and global performance of the blended product. The results of a daily 3-year period experiment show that, although the errors in cross validation (CV) and against an independent dataset (IV) are in general low, the performance of the blended product and also the sensitivity of the parameters are highly influenced by the rain gauge configuration and density. The bandwidth merging parameters increase as the network density is artificially reduced.


Year: 2018

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