Document Type

Article

Publication Title

Water Resources Research

Publisher

Wiley

Rights and Access Note

This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. In addition, no permission is required from the rights-holder(s) for educational uses. For other uses, you need to obtain permission from the rights-holder(s).

Publication Date

7-2015

Publisher location

Hoboken, NJ, USA

First Page

4499

Last Page

4515

Issue Number

6

Volume Number

51

Abstract/ Summary

Changes in seasonality of extreme storms have important implications for public safety, storm water infrastructure, and, in general, adaptation strategies in a changing climate. While past research on this topic offers some approaches to characterize seasonality, the methods are somewhat limited in their ability to discern the diversity of distributional types for extreme precipitation dates. Herein, we present a comprehensive approach for assessment of temporal changes in the calendar dates for extreme precipitation within a circular statistics framework which entails: (a) three measures to summarize circular random variables (traditional approach), (b) four nonparametric statistical tests, and (c) a new nonparametric circular density method to provide a robust assessment of the nature of probability distribution and changes. Two 30 year blocks (1951–1980 and 1981–2010) of annual maximum daily precipitation from 10 stations across the state of Maine were used for our analysis. Assessment of seasonality based on nonparametric approach indicated nonstationarity; some stations exhibited shifts in significant mode toward Spring season for the recent time period while some other stations exhibited multimodal seasonal pattern for both the time periods. Nonparametric circular density method, used in this study, allows for an adaptive estimation of seasonal density. Despite the limitation of being sensitive to the smoothing parameter, this method can accurately characterize one or more modes of seasonal peaks, as well as pave the way toward assessment of changes in seasonality over time.

Citation/Publisher Attribution

Dhakal, N., Jain, S., Gray, A., Dandy, M., & Stancioff, E. 2015. Nonstationarity in seasonality of extreme precipitation: A nonparametric circular statistical approach and its application. Water Resources Research. Volume 51, Issue 6, June 2015, Pages 4499–4515

Publisher Statement

© 2015. American Geophysical Union. All Rights Reserved.

DOI

DOI: 10.1002/2014WR016399

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In Copyright - Educational Use Permitted.