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research
Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data
Authors
Akintug
Allan
+75 more
Arblaster
Arguez
Benjamin J. Henley
Biondi
Box
Cai
Cobb
D'Arrigo
D'Arrigo
Folland
Frost
Gedalof
Gelman
George Kuczera
Haario
Haslett
Heinrich
Hendon
Kiem
Kiem
Koutsoyiannis
Kwon
Lambert
Lavery
Lima
Linsley
Linsley
MacDonald
Mann
Mann
Mantua
Mark A. Thyer
Mauget
McBride
McGowan
McGregor
Mehrotra
Meinke
Meneghini
Micevski
Newman
Parker
Potter
Power
Power
Prairie
Rayner
Saji
Salas
Samuel
Schneider
Schwarz
Sharma
Shen
Solomon
Soon
Speer
Stedinger
Stewart W. Franks
Taylor
Thyer
Thyer
Thyer
Thyer
Tome
Torrence
Ummenhofer
Verdon
Verdon
Verdon
Verdon-Kidd
Vörösmarty
Westra
Whiting
Zhang
Publication date
1 January 2011
Publisher
'American Geophysical Union (AGU)'
Doi
Cite
Abstract
A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. A case study on two catchments in eastern Australia illustrates this framework. To develop an identifiable model characterizing long-term variability for the first level of the hierarchy, paleoclimate proxies, and instrumental indices describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yr is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run lengths, with 90% between 3 and 33 yr and a mean of 15 yr. The Markov chain model, previously used to simulate oscillating wet/dry climate states, is found to underestimate the probability of wet/dry periods >5 yr, and is rejected in favor of a gamma distribution for simulating the run lengths of the wet/dry IPO-PDO states. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. The model is able to replicate observed statistics such as seasonal and multiyear accumulated rainfall distributions and interannual autocorrelations. Mean seasonal rainfall in the IPO-PDO dry states is found to be 15%-28% lower than the wet state at the case study sites. In comparison, an annual lag-one autoregressive model is unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Copyright © 2011 by the American Geophysical Union.Benjamin J. Henley, Mark A. Thyer, George Kuczera and Stewart W. Frank
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