Extraction of Nonlinear Trends in Time Series of Rainfall Using Singular Spectrum Analysis
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 12
Abstract
Characterization of nonlinear trends in time series of hydroclimatic variables exhibiting nonstationarity is necessary for more realistic projections of climate change and for optimal design of hydraulic structures. The present study was conducted to demonstrate the applicability of a novel Monte-Carlo-based singular spectrum analysis (SSA) to characterize nonlinear trends in historical time series of rainfall characteristics. Long-term (1960–2015) rainfall records for 17 gauges located in the Malaprabha River Basin, India, were used to analyze spatiotemporal variabilities of trends in rainfall totals and number of rainy days for annual and seasonal time periods. While the traditional Sen’s Slope and Mann–Kendall (MK) trend tests indicated statistically nonsignificant decreasing monotonic trends at most gauge stations, SSA revealed the existence of steep nonlinear trends and distinct change points in the direction of the trend over the period of record for both rainfall and rainy days. Results of this study demonstrate the potential for SSA to extract crucial information on the trajectories of nonlinear trends and change points in time series of hydroclimatic variables that exhibit nonstationarity.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors are grateful to the Editors and three anonymous reviewers for their critical comments towards improving the quality of the manuscript.
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Journal of Hydrologic Engineering
Volume 25 • Issue 12 • December 2020
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© 2020 American Society of Civil Engineers.
History
Received: Mar 23, 2020
Accepted: Jul 13, 2020
Published online: Oct 15, 2020
Published in print: Dec 1, 2020
Discussion open until: Mar 15, 2021
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