Abstract
AbstractMerging radar and rain gauge rainfall data is a technique used to improve the quality of spatial rainfall estimates and in particular the use of Kriging with External Drift (KED) is a very effective radar‐rain gauge rainfall merging technique. However, kriging interpolations assume Gaussianity of the process. Rainfall has a strongly skewed, positive, probability distribution, characterized by a discontinuity due to intermittency. In KED rainfall residuals are used, implicitly calculated as the difference between rain gauge data and a linear function of the radar estimates. Rainfall residuals are non‐Gaussian as well. The aim of this work is to evaluate the impact of applying KED to non‐Gaussian rainfall residuals, and to assess the best techniques to improve Gaussianity. We compare Box‐Cox transformations with λ parameters equal to 0.5, 0.25, and 0.1, Box‐Cox with time‐variant optimization of λ, normal score transformation, and a singularity analysis technique. The results suggest that Box‐Cox with λ = 0.1 and the singularity analysis is not suitable for KED. Normal score transformation and Box‐Cox with optimized λ, or λ = 0.25 produce satisfactory results in terms of Gaussianity of the residuals, probability distribution of the merged rainfall products, and rainfall estimate quality, when validated through cross‐validation. However, it is observed that Box‐Cox transformations are strongly dependent on the temporal and spatial variability of rainfall and on the units used for the rainfall intensity. Overall, applying transformations results in a quantitative improvement of the rainfall estimates only if the correct transformations for the specific data set are used.
Highlights
Accurate spatially distributed estimates of precipitation are desirable in many water-related fields, like hydrology, water quality monitoring, and water resources management
Merging radar and rain gauge rainfall data is a technique used to improve the quality of spatial rainfall estimates and in particular the use of Kriging with External Drift (KED) is a very effective radar-rain gauge rainfall merging technique
The results suggest that Box-Cox with k 5 0.1 and the singularity analysis is not suitable for KED
Summary
Accurate spatially distributed estimates of precipitation are desirable in many water-related fields, like hydrology, water quality monitoring, and water resources management. Merging weather radar rainfall estimates and rain gauge data has proved to be a successful solution compared to the use of a single source of information (Goudenhoofdt & Delobbe, 2009; Haberlandt, 2007; Jewell & Gaussiat, 2015; Schuurmans et al, 2007; Wilson, 1970). Several techniques have been developed for merging radar and rain gauge data, many of which are based on kriging interpolation methods (Goudenhoofdt & Delobbe, 2009; Li & Heap, 2011; McKee & Binns, 2015). Since the difference between two Gaussian variables results in CECINATI ET AL
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