Abstract
AbstractThis study examines the effect of considering data from rain gauges nearby the boundaries of Galicia (NW Spain) in order to minimize the border effect. Two datasets were considered: the first one comprised 232 climatic stations within Galicia and the second one consisted of 322 rain gauges including the former 232 from Galicia and adding 90 stations from boundary provinces (42 from Asturias, 31 from León and 17 from Zamora). Total monthly rainfall data from 2006 was analyzed and descriptive statistics demonstrated slight differences between both datasets. Theoretical structures were described for all the studied monthly datasets. Spatial dependence analysis showed that the best-fitting semivariogram model structure was the same for both datasets in most of the cases, even though the model parameters showed great differences. Similarly, cross-validation parameter values were clearly distinct among datasets; mostly, the ones corresponding to the 322 stations dataset were closer to the ideal values. Ordinary kriging was performed for both datasets and resulting variance maps showed improvements when the information from boundary regions was taken into account. These improvements can reach up to 25% of the maximum variance value and they were observed in wet months such as January whereas, in dry months such as July, no improvement was observed. Minimum error values were usually lower when extra information was used in the interpolations. In conclusion, a better mapping of the rainfall within a region can be achieved using data from boundary areas, reducing the variance of the estimates.KeywordsMonthly RainfallOrdinary KrigingInverse Distance WeightingDigital Elevation ModelBoundary SiteThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Published Version
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