Monthly runoff prediction using phase space reconstruction

Sivakumar, Bellie; Berndtsson, Ronny; Persson, Magnus

doi:10.1080/02626660109492833

Cited by 64 publications

(36 citation statements)

References 18 publications

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“…This has further been verified and supported by the near-accurate predictions achieved for streamflow data observed at different river systems (e.g. Porporato and Ridolfi, 1997;Jayawardena and Gurung, 2000;Sivakumar et al, 2001aSivakumar et al, , 2002aLisi and Villi, 2001;Islam and Sivakumar, 2002) and also for other hydrologic and geomorphic data, such as lake volume (e.g. Abarbanel and Lall, 1996) and suspended sediment concentration (e.g.…”

Section: Introductionmentioning

confidence: 49%

“…Schreiber and Kantz, 1996), such a theoretical expectation is difficult to meet with when one deals with real data, which are always contaminated with noise. The effect of noise (on prediction/disaggregation) with respect to embedding dimension and number of neighbors are discussed in detail in Sivakumar et al (1999Sivakumar et al ( , 2001aSivakumar et al ( , b, 2002a and, therefore, are not reported herein.…”

Section: Disaggregation Of Flow From 2-day To Dailymentioning

confidence: 99%

“…This is particularly the case in streamflow studies (e.g. Jayawardena and Lai, 1994;Porporato and Ridolfi, 1997;Krasovskaia et al, 1999;Jayawardena and Gurung, 2000;Sivakumar et al, 2001aSivakumar et al, , 2002aLisi and Villi, 2001;Islam and Sivakumar, 2002). For further details, the reader is referred to the articles by Sivakumar (2000Sivakumar ( , 2004.…”

Section: Introductionmentioning

confidence: 99%

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Streamflow disaggregation: a nonlinear deterministic approach

Sivakumar

Wallender

Puente

et al. 2004

Nonlin. Processes Geophys.

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Abstract. This study introduces a nonlinear deterministic approach for streamflow disaggregation. According to this approach, the streamflow transformation process from one scale to another is treated as a nonlinear deterministic process, rather than a stochastic process as generally assumed. The approach follows two important steps: (1) reconstruction of the scalar (streamflow) series in a multidimensional phase-space for representing the transformation dynamics; and (2) use of a local approximation (nearest neighbor) method for disaggregation. The approach is employed for streamflow disaggregation in the Mississippi River basin, USA. Data of successively doubled resolutions between daily and 16 days (i.e. daily, 2-day, 4-day, 8-day, and 16-day) are studied, and disaggregations are attempted only between successive resolutions (i.e. 2-day to daily, 4-day to 2-day, 8-day to 4-day, and 16-day to 8-day). Comparisons between the disaggregated values and the actual values reveal excellent agreements for all the cases studied, indicating the suitability of the approach for streamflow disaggregation. A further insight into the results reveals that the best results are, in general, achieved for low embedding dimensions (2 or 3) and small number of neighbors (less than 50), suggesting possible presence of nonlinear determinism in the underlying transformation process. A decrease in accuracy with increasing disaggregation scale is also observed, a possible implication of the existence of a scaling regime in streamflow.

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Section: Introductionmentioning

confidence: 49%

Section: Disaggregation Of Flow From 2-day To Dailymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Streamflow disaggregation: a nonlinear deterministic approach

Sivakumar

Wallender

Puente

et al. 2004

Nonlin. Processes Geophys.

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“…Jayawardena and Lai, 1994), flood (Laio et al, 2003), and streamflow on daily and monthly scales (e.g. Jayawardena and Lai, 1994;Porporato and Ridolfi, 1996, 2001Liu et al, 1998;Sivakumar et al, 2001Sivakumar et al, , 2002. However, in other fields, especially, medical, there are widespread forecast applications (e.g.…”

Section: Introductionmentioning

confidence: 99%

Local polynomial method for ensemble forecast of time series

Regonda

Rajagopalan

Lall

et al. 2005

Nonlin. Processes Geophys.

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Abstract. We present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the response variable in a space of dimension (D) with a delay time (τ ). To obtain a forecast from a given time point t, three steps are involved: (i) the current state of the system is mapped on to the state space, known as the feature vector, (ii) a small number (K = α * n, α=fraction (0,1] of the data, n=data length) of neighbors (and their future evolution) to the feature vector are identified in the state space, and (iii) a polynomial of order p is fitted to the identified neighbors, which is then used for prediction.

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“…Jayawardena & Lai, 1994;Porporato & Ridolfi, 1997;Krasovskaia et al, 1999;Stehlik, 1999;Sivakumar et al, 2002); the possibility of accurate river flow predictions using nonlinear deterministic approaches (e.g. Porporato & Ridolfi, 1997;Sivakumar et al, 2001Sivakumar et al, , 2002; and the superiority of these approaches over stochastic ones (e.g. Jayawardena & Gurung, 2000;Lisi & Villi, 2001).…”

Section: Introductionmentioning

confidence: 99%

Temporal scaling in river flow: can it be chaotic? / L’invariance d’échelle de l’écoulement fluvial peut-elle être chaotique?

Regonda

Sivakumar

Jain

2004

Hydrological Sciences Journal

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Identification of the presence of scaling in the river flow process has been a challenging problem in hydrology. Studies conducted thus far have viewed this problem essentially from a stochastic perspective, because the river flow process has traditionally been assumed to be a result of a very large number of variables. However, recent studies employing nonlinear deterministic and chaotic dynamic concepts have reported that the river flow process could also be the outcome of a deterministic system with only a few dominant variables. In the wake of such reports, a preliminary attempt is made in this study to investigate the type of scaling behaviour in the river flow process (i.e. chaotic or stochastic). The investigation is limited only to temporal scaling. Flow data of three different scales (daily, 5-day and 7-day) observed in each of three rivers in the USA: the Kentucky River in Kentucky, the Merced River in California and the Stillaguamish River in Washington, are analysed. It is assumed that the dynamic behaviour of the river flow process at these individual scales provides clues about the scaling behaviour between these scales. The correlation dimension is used as an indicator to distinguish between chaotic and stochastic behaviours. The results are mixed with regard to the type of flow behaviour at individual scales and, hence, to the type of scaling behaviour, as some data sets show chaotic behaviour while others show stochastic behaviour. They suggest that characterization (chaotic or stochastic) of river flow should be a necessary first step in any scaling study, as it could provide important information on the appropriate approach for data transformation purposes. caractérisation (chaotique ou stochastique) de l'écoulement fluvial devrait être la première étape de toute étude de l'invariance d'échelle, puisqu'elle peut fournir une information importante sur l'approche pertinente à suivre pour la transformation des données. Key words

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Monthly runoff prediction using phase space reconstruction

Cited by 64 publications

References 18 publications

Streamflow disaggregation: a nonlinear deterministic approach

Streamflow disaggregation: a nonlinear deterministic approach

Local polynomial method for ensemble forecast of time series

Temporal scaling in river flow: can it be chaotic? / L’invariance d’échelle de l’écoulement fluvial peut-elle être chaotique?

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