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Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data

Citation

Henley, BJ and Thyer, M and Kuczera, G and Franks, SW, Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data, Water Resources Research, 47 pp. 1-14. ISSN 1944-7973 (2011) [Refereed Article]


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Copyright Statement

Copyright 2011 by the American Geophysical Union.

DOI: doi:10.1029/2010WR010034

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.

Item Details

Item Type:Refereed Article
Keywords:climate variability change risk rainfall model
Research Division:Engineering
Research Group:Environmental Engineering
Research Field:Environmental Engineering Modelling
Objective Division:Environment
Objective Group:Land and Water Management
Objective Field:Land and Water Management of environments not elsewhere classified
Author:Franks, SW (Professor Stewart Franks)
ID Code:86418
Year Published:2011
Web of Science® Times Cited:25
Deposited By:Engineering
Deposited On:2013-09-14
Last Modified:2013-10-28
Downloads:0

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