Stochastic simulation model for nonstationary time series using an autoregressive wavelet decomposition: Applications to rainfall and temperature

Kwon, Hyun‐Han; Lall, Upmanu; Khalil, Abedalrazq F.

doi:10.1029/2006wr005258

Cited by 104 publications

(96 citation statements)

References 63 publications

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“…Application with the decadal time scale in mind, the focus of the work described here, has also been undertaken [Prairie et al, 2008;Kwon et al, 2007Kwon et al, , 2009], but constitutes a less well explored domain. A focus on the decadal scale shifts the emphasis toward regional low-frequency variability and its potential for augmenting (or compensating) secular, forced climate change on decadal time horizons.…”

Section: Introductionmentioning

confidence: 99%

Stochastic decadal climate simulations for the Berg and Breede Water Management Areas, Western Cape province, South Africa

Greene

Hellmuth

Lumsden

2012

Water Resources Research

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[1] A method is described for the generation of multivariate stochastic climate sequences for the Berg and Breede Water Management Areas in the Western Cape province of South Africa. The sequences, based on joint modeling of precipitation and minimum and maximum daily temperatures, are conditioned on annualized data, the aim being to simulate realistic variability on annual to decadal time scales. A vector autoregressive (VAR) model is utilized for this purpose and reproduces well those statistical attributes, including intervariable correlation and serial autocorrelation in individual variables, most relevant for the regional climate in this setting. The sequences incorporate nonlinear climate change trends, inferred using an ensemble of global climate models from the Coupled Model Intercomparison Project (CMIP5). Subannual variability is simulated using a block resampling scheme based on the k-nearest-neighbor approach, preserving both temporal patterns and spatial correlations. Downscaling to a network of quinary-level catchments enables distributed runoff, streamflow, and crop simulations and the assessment and integration of impacts. Final output takes the form of daily sequences, structured for driving the ACRU agrohydrological model of the University of KwaZulu-Natal, South Africa.

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Section: Introductionmentioning

confidence: 99%

Stochastic decadal climate simulations for the Berg and Breede Water Management Areas, Western Cape province, South Africa

Greene

Hellmuth

Lumsden

2012

Water Resources Research

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“…However, many data-driven models cannot fully meet these needs. For instance, linear regression (LR) models provide only reasonable accuracy and suffer from the assumptions of stationarity and linearity [10]. Being different from LR models, the artificial neural network (ANN) models can learn…”

Section: Introductionmentioning

confidence: 99%

Discussion on the Choice of Decomposition Level for Wavelet Based Hydrological Time Series Modeling

Sang

Liu

Wang

2016

Water

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Abstract:The combination of wavelet analysis methods with data-driven models is a prevalent approach to conducting hydrological time series forecasting, but the results are affected by the accuracy of the wavelet decomposition of the series. The choice of decomposition level is one of the key factors for the wavelet decomposition. In this paper, the data of daily precipitation and streamflow time series measured in the upper reach of the Heihe River Basin in Northwest China were used as an example, and the influence of the decomposition level on wavelet-based hydrological time series forecasting was investigated. The true components of the precipitation series were identified, and the modeling results using 10 decomposition levels and two decomposition types were compared. The results affirmed that the wavelet-based modeling performance is sensitive to the choice of decomposition level, which is determined by the time series analyzed, but has no relation with the decomposition type used. The essence of the choice of decomposition level is to reveal the complex variability of hydrological time series under multi-temporal scales, and first knowing the true components of series could guide the choice of decomposition level. Through this study, the relationship among original series' characteristics, the choice of decomposition level, and the accuracy of wavelet-based hydrological time series forecasting can be more clearly understood, and it can be an improvement for wavelet-based data-driven modeling.

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“…However, some have been focused on quantifying the effects of limited satellite overpasses (Bell 1987;Bell et al 1990;Astin 1997;Steiner et al 2003;Gebremichael and Krajewski 2004), some have not considered the spatial correlation of precipitation and errors (Bardossy 1998;Kwon et al 2007;Gomi and Kuzuha 2013), some are only precipitation simulation rather than conditional precipitation estimation (Sivapalan and Wood 1987;Wheater et al 2000;Cowpertwait et al 2002;Ferraris et al 2003), and some others use parametric distributions for characterizing the space-time variability of rainfall (Gupta and Waymire 1993;Nykanen and Harris 2003;Bellerby and Sun 2005;Hossain and Anagnostou 2006;Clark and Slater 2006;Teo and Grimes 2007;Grimes 2008;AghaKouchak et al 2010a;Paschalis et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

Alemohammad

McLaughlin

Entekhabi

2015

Monthly Weather Review

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Ensemble-based data assimilation techniques are often applied to land surface models in order to estimate components of terrestrial water and energy balance. Precipitation forcing uncertainty is the principal source of spread among the ensembles that is required for utilizing information in observations to correct model priors. Precipitation fields may have both position and magnitude errors. However, current uncertainty characterizations of precipitation forcing in land data assimilation systems often do no more than applying multiplicative errors to precipitation fields. In this paper, an ensemble-based Bayesian method for characterization of uncertainties associated with precipitation retrievals from spaceborne instruments is introduced. This method is used to produce stochastic replicates of precipitation fields that are conditioned on precipitation observations. Unlike previous studies, the error likelihood is derived using an archive of historical measurements. The ensemble replicates are generated using a stochastic method, and they are intermittent in space and time. The replicates are first projected in a low-dimension subspace using a problem-specific set of attributes. The attributes are derived using a dimensionality reduction scheme that takes advantage of singular value decomposition. A nonparametric importance sampling technique is formulated in terms of the attribute vectors to solve the Bayesian sampling problem. Examples are presented using retrievals from operational passive microwave instruments, and performance of the method is assessed using ground validation measurements from a surface weather radar network. Results indicate that this ensemble characterization approach provides a useful description of precipitation uncertainties with a posterior ensemble that is narrower in distribution than its prior while containing both precipitation position and magnitude errors.

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Stochastic simulation model for nonstationary time series using an autoregressive wavelet decomposition: Applications to rainfall and temperature

Cited by 104 publications

References 63 publications

Stochastic decadal climate simulations for the Berg and Breede Water Management Areas, Western Cape province, South Africa

Stochastic decadal climate simulations for the Berg and Breede Water Management Areas, Western Cape province, South Africa

Discussion on the Choice of Decomposition Level for Wavelet Based Hydrological Time Series Modeling

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

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