The Hurst Exponent of Precipitation

17 Pages Posted: 27 Nov 2015  

Jamal Munshi

Sonoma State University

Date Written: November 2015

Abstract

Rescaled range analysis of precipitation in the sample period 1893-2014 for ten USHCN stations in five states of the USA does not provide evidence of dependence, long term memory, or persistence in the time series. All of the observed Hurst exponents of precipitation are indicative of Gaussian randomness. Therefore, multi-decadal and non-periodic drought and flood events observed at some of these stations are more likely to be irregular cyclical phenomena of nature than the random effects of persistence and long term memory in the data.

Keywords: global warming, climate change, greenhouse effect, OLS trends, Hurst exponent, persistence, Brownian motion, structural properties of time series data, long term memory, rainfall, precipitation, drought, floods, hydrology

Suggested Citation

Munshi, Jamal, The Hurst Exponent of Precipitation (November 2015). Available at SSRN: https://ssrn.com/abstract=2695753 or http://dx.doi.org/10.2139/ssrn.2695753

Jamal Munshi (Contact Author)

Sonoma State University ( email )

1801 East Cotati Avenue
Rohnert Park, CA 94928
United States

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