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dc.contributor.author
Lange, Holger
dc.contributor.author
Sippel, Sebastian
dc.contributor.author
Rosso, Osvaldo Aníbal
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dc.date.available
2020-03-12T21:59:19Z
dc.date.issued
2018-07
dc.identifier.citation
Lange, Holger; Sippel, Sebastian; Rosso, Osvaldo Aníbal; Nonlinear dynamics of river runoff elucidated by horizontal visibility graphs; American Institute of Physics; Chaos; 28; 7; 7-2018; 1-13; 075520
dc.identifier.issn
1054-1500
dc.identifier.uri
http://hdl.handle.net/11336/99427
dc.description.abstract
Horizontal Visibility Graphs (HVGs) are a recently developed method to construct networks from time series. The values of the time series are considered as the nodes of the network and are linked to each other if there is no larger value between them, such as they can “see” each other. The network properties reflect the nonlinear dynamics of the time series. For some classes of stochastic processes and for periodic time series, analytical results can be obtained for network-derived quantities such as the degree distribution, the local clustering coefficient distribution, the mean path length, and others. HVGs have the potential to discern between deterministic-chaotic and correlated-stochastic time series. Here, we investigate the sensitivity of the HVG methodology to properties and pre-processing of real-world data, i.e., time series length, the presence of ties, and deseasonalization, using a set of around 150 runoff time series from managed rivers at daily resolution from Brazil with an average length of 65 years. We show that an application of HVGs on real-world time series requires a careful consideration of data pre-processing steps and analysis methodology before robust results and interpretations can be obtained. For example, one recent analysis of the degree distribution of runoff records reported pronounced sub-exponential “long-tailed” behavior of North American rivers, whereas another study of South American rivers showed hyper-exponential “short-tailed” behavior resembling correlated noise. We demonstrate, using the dataset of Brazilian rivers, that these apparently contradictory results can be reconciled by minor differences in data-preprocessing (here: small differences in subtracting the seasonal cycle). Hence, data-preprocessing that is conventional in hydrology (“deseasonalization”) changes long-term correlations and the overall runoff dynamics substantially, and we present empirical consequences and extensive simulations to investigate these issues from a HVG methodological perspective. After carefully accounting for these methodological aspects, the HVG analysis reveals that the river runoff dataset shows indeed complex behavior that appears to stem from a superposition of short-term correlated noise and “long-tailed behaviour,” i.e., highly connected nodes. Moreover, the construction of a dam along a river tends to increase short-term correlations in runoff series. In summary, the present study illustrates the (often substantial) effects of methodological and data-preprocessing choices for the interpretation of river runoff dynamics in the HVG framework and its general applicability for real-world time series.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
American Institute of Physics
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
RIVER RUNOFF
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HORIZANTAL VISIBILITY GRAPH
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NONLINEAR DYNAMICS
dc.subject.classification
Otras Ciencias Físicas
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dc.subject.classification
Ciencias Físicas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Nonlinear dynamics of river runoff elucidated by horizontal visibility graphs
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-03-05T14:56:06Z
dc.journal.volume
28
dc.journal.number
7
dc.journal.pagination
1-13; 075520
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Lange, Holger. Norwegian Institute of Bioeconomy Research; Noruega
dc.description.fil
Fil: Sippel, Sebastian. Norwegian Institute of Bioeconomy Research; Noruega
dc.description.fil
Fil: Rosso, Osvaldo Aníbal. Instituto Universidad Escuela de Medicina del Hospital Italiano; Argentina. Universidad de Los Andes; Chile. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Chaos
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1063/1.5026491
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://aip.scitation.org/doi/10.1063/1.5026491
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