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Title of Journal: Stoch Environ Res Risk Assess

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Abbravation: Stochastic Environmental Research and Risk Assessment

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Springer Berlin Heidelberg

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DOI

10.1007/s11605-015-2832-1

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1436-3259

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Regionalization of precipitation characteristics i

Authors: Z E Asong M N Khaliq H S Wheater
Publish Date: 2014/06/28
Volume: 29, Issue: 3, Pages: 875-892
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Abstract

Observed data at most stations are often inadequate to obtain reliable estimates of many hydrometeorological variables that not only define water availability across a region but also the vulnerability of social infrastructure to climatic extremes To overcome this data from neighboring sites with similar statistical characteristics are often pooled The pooling process is based on partitioning of a larger region into smaller subregions with homogeneous features of interest The established approaches rely heavily on statistics computed from observed precipitation data rather than the covariates that play a significant role in modulating the regional and local climate patterns at various temporal and spatial scales In this study a new approach for identifying homogeneous regions for regionalization of precipitation characteristics is proposed for the Canadian Prairie Provinces This approach incorporates information about largescale atmospheric covariates teleconnection indices and geographical site attributes that impact spatial patterns of precipitation in order to delineate homogeneous precipitation regions through combined use of multivariate approaches—principal component analysis canonical correlation analysis and fuzzy Cmeans clustering Results of the analyses suggest that the study area can be partitioned into five homogeneous regions These partitions are validated independently for homogeneity using statistics computed from monthly and seasonal precipitation totals and seasonal extremes from a network of observation stations Furthermore based on the identified regions precipitation magnitudefrequency relationships of warm and cold season single and multiday precipitation extremes developed through regional frequency analysis are mapped spatially Such estimates are important for numerous water resources related activitiesThe financial support from the Global Institute for Water Security and School of Environment and Sustainability is gratefully acknowledged Thanks are due to Eva Mekis from Environment Canada for providing access to adjusted precipitation dataset used in this study We would like to express our thanks to Jonathan Hosking for his advice on the defuzzification of the fuzzy soft clusters prior to applying the regional frequency analysis algorithm Thanks are also due to Sun Chun for useful comments which helped improve the analyses presented in this paper The authors further thank three anonymous reviewers for their helpful comments on the manuscript

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