Analysis of space–time patterns of rainfall events during 1996–2008 in Yilan County (Taiwan)
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Understanding local precipitation patterns is essential to water resource management and flood mitigation. Precipitation patterns can vary in space and time depending on factors from different spatial scales such as local topographical changes and macroscopic atmospheric circulation. This study applied the two-stage classification method to distinguish the space–time patterns of local precipitations in the two identified distinct synoptic conditions, i.e. summer and autumn, from 24 gauges during 1996–2008 in Yilan County, Taiwan. The proposed method classifies the synoptic and local conditions for the space–time rainfall patterns by using K-means coupled with empirical orthogonal function analysis, and hierarchical ascending clustering method respectively. The proposed two-stage classification method considers not only the magnitude and the space–time distribution of rainfall events, but also the associated synoptic conditions. The results identified three primary patterns of extreme and two patterns of normal events in both seasons. Regarding the extreme events from typhoons, wind directions and the frontal accompanied effect are major contributors to the magnitude and spatial distribution of rainfall events in the summer and autumn, respectively. Spatiotemporal covariance structures are used to characterize the variability of normal events, showing the increasing frequency of wide spatial and temporal ranges from the summer to autumn. In summary, the proposed classification analysis provides patterns associated with distinct underlying physical mechanisms and space–time characteristics. The general characteristics of rainfall patterns can provide insights for the hydrological modeling of local catchments under different climatic scenarios.
KeywordsPrecipitation classification Space–time classification Rainfall process
This research is supported by grants from the Ministry of Science and Technology of Taiwan (NSC101-2628-E-002-017-MY3 and NSC102-2221-E-002-140-MY3) and the Central Weather Bureau of Taiwan (MOTC-CWB-99-2M-10). We would also like to thank Cheng-Kan Wang and Tu-Je Chang for their initial implementations of the data analysis.
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