Analysis of high-resolution rainfall data

Georgakakos, Konstantine P.; Sharifi, M B; Sturdevant, P. L.

doi:10.1017/cbo9780511564482.012

Cited by 7 publications

(5 citation statements)

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“…At this point, it is relevant to note that the Grassberger-Procaccia correlation dimension algorithm (and others) has been employed in a number of studies investigating the nature of rainfall (and other phenomena), whether stochastic or deterministic chaotic (e.g. Hense, 1987;Rodriguez-Iturbe et al, 1989;Sharifi et al, 1990;Lorenz, 1991;Islam et al, 1993;Tsonis et al, 1993;Berndtsson et al, 1994;Jayawardena and Lai, 1994;Georgakakos et al, 1995;Koutsoyiannis and Pachakis, 1996;Puente and Obregon, 1996;Sivakumar et al, 1999aSivakumar et al, ,b, 2001. The majority of these studies have reported the existence of low-dimensional chaotic behaviour, but the reliability of such reports has been questioned based on a host of issues (e.g.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear analysis of rainfall dynamics in California's Sacramento Valley

et al. 2006

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Abstract:This study investigates the dynamic nature of rainfall observed at the Sustainable Agriculture Farming Systems (SAFS) site in California's Sacramento Valley, which was established to study the benefits of winter cover cropping in Mediterranean irrigated-arid systems. Rainfall data of four different temporal scales (i.e. daily, weekly, biweekly, and monthly) are analysed to determine the dynamic nature of precipitation in time. In an arid climate with seasonal precipitation this has large implications for land and water management, both in the short term and in the long term. A nonlinear dynamic technique (correlation dimension method) that uses the phase-space reconstruction and dimension concepts is employed. Bearing in mind the possible effects of the presence of zeros (i.e. no rain) on the outcomes of this analysis, an attempt is also made to compare the dynamic nature of all-year rainfall and winter rainfall. Analysis of 15 years of data suggests that rainfall dynamics at this site are dominated by a large number of variables, regardless of the scales and seasons studied. The dimension results also suggest that: (1) rainfall dynamics at coarser resolutions are more irregular than that at finer resolutions; (2) winter rainfall has a higher variability than all-year rainfall. These results are indeed useful to gain information about the complexity of the rainfall process at this site with respect to (temporal) scales and seasons and, hence, the appropriate model (high-dimensional) type. However, in view of the potential effects of certain rainfall data characteristics (e.g. zeros, measurement errors, scale effects) on the correlation dimension analysis, the discussion also emphasizes the need for further verification, and possibly confirmation, of these results.

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Section: Introductionmentioning

confidence: 99%

Nonlinear analysis of rainfall dynamics in California's Sacramento Valley

et al. 2006

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“…However, higher correlation dimension data sets are not uncommonly encountered in hydrology. For example, Keppenne and Nicolis (1989) observed the correlation dimension vary from 7.4 to 8.3 for the daily geopotential data set from nine stations in Europe while Georgakakos et al (1995) observed that the correlation dimension to vary from 2.8 to 7.9 for a high-intensity storm data set in Iowa City, USA. It is noted that the data set on high-intensity storm was observed to have impacts on the correlation dimension similar to streamflow data consisting of outliers.…”

Section: Correlation Dimensionmentioning

confidence: 99%

Chaos based Analytical techniques for daily extreme hydrological observations

Panu

Lennox

2007

Journal of Hydrology

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“…The observation of chaotic behaviour in hydrological processes (e.g. Hense, 1987;Rodriguez-Iturbe et al, 1989;Sharifi et al, 1990;Islam et al, 1993;Tsonis et al, 1993;Berndtsson et al, 1994;Jayawardena & Lai, 1994;Georgakakos et al, 1995;Porporato & Ridolfi, 1996Puente & Obregon, 1996;Sangoyomi et al, 1996;Sivakumar, 1999;Sivakumar et al, 1999;Stehlik, 1999;Krasovskaia et al, 1999) supports such concerns. Following the ideas gained from chaos theory, and the fact that hydrological processes could possess chaotic dynamics, it is reasonable to conjecture that any small disturbances (or errors) in the initial conditions, say, in rainfall, could ultimately undermine one's ability to model and predict long-term dynamics of the rainfall-runoff process.…”

Section: Introductionmentioning

confidence: 97%

“…In other words, the studies have been limited either to the rainfall process alone (e.g. Hense, 1987;Rodriguez-Iturbe et al, 1989;Sharifi et al, 1990;Islam et al, 1993;Tsonis et al, 1993;Berndtsson et al, 1994;Jayawardena & Lai, 1994;Georgakakos et al, 1995;Puente & Obregon, 1996;Sivakumar, 1999;Sivakumar et al, 1999a), or to the runoff process alone (e.g. Jayawardena & Lai, 1994;Porporato & Ridolfi, 1996Liu et al, 1998;Wang & Gan, 1998, Stehlik, 1999Krasovskaia et al, 1999), but not to the rainfall-runoff process as a whole in a basin.…”

Section: Introductionmentioning

confidence: 99%

Evidence of chaos in the rainfall-runoff process

Sivakumar¹,

Berndtsson²,

Olsson³

et al. 2001

Hydrological Sciences Journal

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The transformation of rainfall into runoff is one of the most important processes in hydrology. In the past few decades, a wide variety of automated or computer-based approaches have been applied to model this process. However, many such approaches have an important limitation in that they treat the rainfall-runoff process as a realization of only a few parameters of linear relationships rather than the process as a whole. What is required, therefore, is an approach that can capture not only the overall appearance but also the intricate details of the nonlinear behaviour of the process. The purpose of this study is to investigate the possibility of understanding the dynamics of the rainfall-runoff process from a new perspective, as a chaotic process. The possible existence of chaotic behaviour in the rainfall-runoff process is studied by investigating the rainfall and runoff time series: (a) separately; and (b) jointly (using the runoff coefficient). Monthly rainfall and runoff observed over a period of 131 years (January 1807-December 1937) at the Gôta River basin in the south of Sweden are analysed. The correlation dimension method is employed to identify the presence of chaos. The correlation dimensions obtained for the rainfall and runoff time series are 6.4 and 5.5, respectively. The finite dimensions obtained for the rainfall and runoff time series indicate the possible existence of chaos in these processes, implying that the joint rainfall-runoff process might also exhibit chaotic behaviour. The correlation dimension of about 7.8 obtained for the runoff coefficient also indicates the possible presence of chaos and supports the above results.Key words rainfall-runoff models; runoff coefficient; chaotic dynamics; Gôta River. Sweden; nonlinear analysis; time series analysis; correlation dimension Preuve de l'existence de chaos dans le processus pluie-débit Résumé La transformation de la pluie en débit est l'un des plus importants processus hydrologiques. Au cours des dernières décennies un grand nombre d'approches automatisées ou informatisées ont été mises en oeuvre pour modéliser ce processus. Ces approches demeurent toutefois limitées, car elles traitent le processus pluie-débit comme dépendant de quelques paramètres attachés à des relations linéaires au lieu de considérer ce processus comme un tout. Une approche permettant de saisir non seulement les grands traits du processus mais aussi la complexité des détails résultant de son comportement non linéaire est nécessaire. Le but de la présente étude est de chercher à comprendre la dynamique du processus pluie-débit dans une nouvelle perspective, celle des processus chaotiques. On a cherché à mettre en évidence l'existence éventuelle d'un comportement chaotique en étudiant des séries chronologiques de pluies et de débits: (a) séparément; et (b) conjointement (en utilisant le coefficient de ruissellement). Nous avons étudié les pluies et les débits mensuels observés pendant une période de 131 ans (Janvier 1807-Décembre 1937) sur le bassin de la rivi...

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Analysis of high-resolution rainfall data

Cited by 7 publications

References 0 publications

Nonlinear analysis of rainfall dynamics in California's Sacramento Valley

Nonlinear analysis of rainfall dynamics in California's Sacramento Valley

Chaos based Analytical techniques for daily extreme hydrological observations

Evidence of chaos in the rainfall-runoff process

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