Applications of Box-Jenkins methods of time series analysis to the reconstruction of drought from tree rings

Persistent Link:
http://hdl.handle.net/10150/191062
Title:
Applications of Box-Jenkins methods of time series analysis to the reconstruction of drought from tree rings
Author:
Meko, David Michael.
Issue Date:
1981
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
The lagged responses of tree-ring indices to annual climatic or hydrologic series are examined in this study. The objectives are to develop methods to analyze the lagged responses of individual tree-ring indices, and to improve upon conventional methods of adjusting for the lag in response in regression models to reconstruct annual climatic or hydrologic series. The proposed methods are described and applied to test data from Oregon and Southern California. Transfer-function modeling is used to estimate the dependence of the current ring on past years' climate and to select negative lags for reconstruction models. A linear system is assumed; the input is an annual climatic variable, and the output is a tree-ring index. The estimated impulse response function weights the importance of past and current years' climate on the current year's ring. The identified transfer function model indicates how many past years' rings are necessary to account for the effects of past years' climate. Autoregressive-moving-average (ARMA) modeling is used to screen out climatically insensitive tree-ring indices, and to estimate the lag in response to climate unmasked from the effects of autocorrelation in the tree-ring and climatic series. The climatic and tree-ring series are each prewhitened by ARMA models, and crosscorrelation between the ARMA residuals are estimated. The absence of significant crosscorrelations Implies low sensitivity. Significant crosscorrelations at lags other than zero indicate lag in response. This analysis can also aid in selecting positive lags for reconstruction models. An alternative reconstruction method that makes use of the ARMA residuals is also proposed. The basic concept is that random (uncorrelated in time) shocks of climate induce annual random shocks of tree growth, with autocorrelation in the tree-ring index resulting from inertia in the system. The steps in the method are (1) fit ARMA models to the tree-ring index and the climatic variable, (2) regress the ARMA residuals of the climatic variable on the ARMA residuals of the treering index, (3) substitute the long-term prewhitened tree-ring index into the regression equation to reconstruct the prewhitened climatic variable, and (4) build autocorrelation back into the reconstruction with the ARMA model originally fit to the climatic variable. The trial applications on test data from Oregon and Southern California showed that the lagged response of tree rings to climate varies greatly from site to site. Sensitive tree-ring series commonly depend significantly only on one past year's climate (regional rainfall index). Other series depend on three or more past years' climate. Comparison of reconstructions by conventional lagging of predictors with reconstructions by the random-shock method indicate that while the lagged models may reconstruct the amplitude of severe, long-lasting droughts better than the random-shock model, the random-shock model generally has a flatter frequency response. The random-shock model may therefore be more appropriate where the persistence structure is of prime interest. For the most sensitive series with small lag in response, the choice of reconstruction method makes little difference in properties of the reconstruction. The greatest divergence is for series whose impulse response weights from the transfer function analysis do not die off rapidly with time.
Type:
Dissertation-Reproduction (electronic); text
Keywords:
Hydrology.; Dendroclimatology -- California -- Mathematical models.; Dendroclimatology -- Oregon -- Mathematical models.; Precipitation (Meteorology) -- Measurement -- Statistical methods.; Tree-rings -- Mathematical models.
Degree Name:
Ph. D.
Degree Level:
doctoral
Degree Program:
Hydrology and Water Resources; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Stockton, Charles W.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleApplications of Box-Jenkins methods of time series analysis to the reconstruction of drought from tree ringsen_US
dc.creatorMeko, David Michael.en_US
dc.contributor.authorMeko, David Michael.en_US
dc.date.issued1981en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThe lagged responses of tree-ring indices to annual climatic or hydrologic series are examined in this study. The objectives are to develop methods to analyze the lagged responses of individual tree-ring indices, and to improve upon conventional methods of adjusting for the lag in response in regression models to reconstruct annual climatic or hydrologic series. The proposed methods are described and applied to test data from Oregon and Southern California. Transfer-function modeling is used to estimate the dependence of the current ring on past years' climate and to select negative lags for reconstruction models. A linear system is assumed; the input is an annual climatic variable, and the output is a tree-ring index. The estimated impulse response function weights the importance of past and current years' climate on the current year's ring. The identified transfer function model indicates how many past years' rings are necessary to account for the effects of past years' climate. Autoregressive-moving-average (ARMA) modeling is used to screen out climatically insensitive tree-ring indices, and to estimate the lag in response to climate unmasked from the effects of autocorrelation in the tree-ring and climatic series. The climatic and tree-ring series are each prewhitened by ARMA models, and crosscorrelation between the ARMA residuals are estimated. The absence of significant crosscorrelations Implies low sensitivity. Significant crosscorrelations at lags other than zero indicate lag in response. This analysis can also aid in selecting positive lags for reconstruction models. An alternative reconstruction method that makes use of the ARMA residuals is also proposed. The basic concept is that random (uncorrelated in time) shocks of climate induce annual random shocks of tree growth, with autocorrelation in the tree-ring index resulting from inertia in the system. The steps in the method are (1) fit ARMA models to the tree-ring index and the climatic variable, (2) regress the ARMA residuals of the climatic variable on the ARMA residuals of the treering index, (3) substitute the long-term prewhitened tree-ring index into the regression equation to reconstruct the prewhitened climatic variable, and (4) build autocorrelation back into the reconstruction with the ARMA model originally fit to the climatic variable. The trial applications on test data from Oregon and Southern California showed that the lagged response of tree rings to climate varies greatly from site to site. Sensitive tree-ring series commonly depend significantly only on one past year's climate (regional rainfall index). Other series depend on three or more past years' climate. Comparison of reconstructions by conventional lagging of predictors with reconstructions by the random-shock method indicate that while the lagged models may reconstruct the amplitude of severe, long-lasting droughts better than the random-shock model, the random-shock model generally has a flatter frequency response. The random-shock model may therefore be more appropriate where the persistence structure is of prime interest. For the most sensitive series with small lag in response, the choice of reconstruction method makes little difference in properties of the reconstruction. The greatest divergence is for series whose impulse response weights from the transfer function analysis do not die off rapidly with time.en_US
dc.description.notehydrology collectionen_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.typetexten_US
dc.subjectHydrology.en_US
dc.subjectDendroclimatology -- California -- Mathematical models.en_US
dc.subjectDendroclimatology -- Oregon -- Mathematical models.en_US
dc.subjectPrecipitation (Meteorology) -- Measurement -- Statistical methods.en_US
dc.subjectTree-rings -- Mathematical models.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairStockton, Charles W.en_US
dc.contributor.committeememberLamarche, Jr., V. C.en_US
dc.contributor.committeememberInce, Simonen_US
dc.contributor.committeememberDavis, Donald R.en_US
dc.contributor.committeememberSimpson, Eugene S.en_US
dc.identifier.oclc213298714en_US
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