Streamflow disaggregation: a nonlinear deterministic approach

Sivakumar, Bellie; Wallender, W. W.; Puente, Carlos E.; Islam, Md. Nazrul

doi:10.5194/npg-11-383-2004

Cited by 21 publications

(12 citation statements)

References 37 publications

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“…Similar results on the effects of scale on hydrologic process complexity (i.e. increase in complexity or change from determinism to stochasticity with increasing time scale) were also observed by a few other studies as well (e.g., Sivakumar et al 2004Sivakumar et al , 2006Salas et al 2005;Sivakumar and Chen 2007), albeit in different contexts and employing different methodologies to different systems and processes (including rainfall, river flow and sediment load). There may indeed be exceptions to this situation with no trend possibly observed in the 'scale versus complexity' relationship [see Sivakumar et al (2001b) for details], since this relationship essentially depends on, for example, rainfall characteristics (e.g.…”

Section: Scale and Scale-invariancesupporting

confidence: 74%

Nonlinear dynamics and chaos in hydrologic systems: latest developments and a look forward

Sivakumar

2008

Stoch Environ Res Risk Assess

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During the last two decades or so, studies on the applications of the concepts of nonlinear dynamics and chaos to hydrologic systems and processes have been on the rise. Earlier studies on this topic focused mainly on the investigation and prediction of chaos in rainfall and river flow, and further advances were made during the subsequent years through applications of the concepts to other problems (e.g. data disaggregation, missing data estimation, and reconstruction of system equations) and other processes (e.g. rainfall-runoff and sediment transport). The outcomes of these studies are certainly encouraging, especially considering the exploratory stage of the concepts in hydrologic sciences. This paper discusses some of the latest developments on the applications of these concepts to hydrologic systems and the challenges that lie ahead on the way to further progress. As for their applications, studies in the important areas of scaling, groundwater contamination, parameter estimation and optimization, and catchment classification are reviewed and the inroads made thus far are reported. In regards to the challenges that lie ahead, particular focus is given to improving our understanding of these largely less-understood concepts and also finding ways to integrate these concepts with the others. With the recognition that none of the existing one-sided 'extremeview' modeling approaches is capable of solving the hydrologic problems that we are faced with, the need for finding a balanced 'middle-ground' approach that can integrate different methods is stressed. To this end, the viability of bringing together the stochastic concepts and the deterministic concepts as a starting point is also highlighted.

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Section: Scale and Scale-invariancesupporting

confidence: 74%

Nonlinear dynamics and chaos in hydrologic systems: latest developments and a look forward

Sivakumar

2008

Stoch Environ Res Risk Assess

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“…This obviously necessitates understanding and/or ''transforming'' the process of interest at the scale of interest from the corresponding process at another scale (which may be either upscaling or downscaling). Extensive details of the scale issue in hydrology are already available in the literature (e.g., Gupta andWaymire 1990, 1998;Gupta et al 1994Gupta et al , 1996Blöschl and Sivapalan 1995;Gupta and Dawdy 1995;Kalma and Sivapalan 1996;Gupta 2004;Sivakumar et al 2004), and therefore are not reported herein.…”

Section: Hydrologic Systems and Current Modeling Trendmentioning

confidence: 96%

“…For instance, the phase-space diagram reflects the increasing complexity in the dynamic evolution of flow (in the Mississippi River basin) with aggregation of data in temporal scale (from daily to 16-day), a possible implication that the actual (number of) dominant processes are different at these different scales. This observation is particularly interesting, since our general intuition is that complexity of a system decreases with aggregation in temporal scale [see Sivakumar et al (2004) for further details, especially through a data disaggregation approach].…”

Section: Catchment Classification Framework: Role Of Dominant Processesmentioning

confidence: 96%

Dominant processes concept, model simplification and classification framework in catchment hydrology

Sivakumar

2007

Stoch Environ Res Risk Assess

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Technological and methodological advances have facilitated tremendous growth in hydrology during the last century; however, there are also concerns that these advances indirectly contribute to additional problems in our research. An insight into hydrologic literature reveals our tendency to develop more complex models than perhaps needed, and our increasing emphasis on individual mathematical techniques rather than general hydrologic issues. Some recent studies of diverse forms have suggested that simplification in modeling and development of a common framework may help alleviate these problems. The present study is intended to bring such studies together towards a more coherent approach to research in catchment hydrology. This is done by highlighting the need for model simplification and generalization and proposing some potential directions for achieving such. Through a discussion of difficulties in data measurements, the need for moving beyond the notion of ''modeling everything'' to the notion of ''capturing the essential features'' is explained; the concept of dominant processes in model simplification and the utility of integration of concepts for modeling improvement are discussed. Formulation of a catchment classification framework is advocated as a possible means for a common framework in hydrology, and the role of dominant processes in this formulation is presented; the problems due to adoption of different modeling terminologies are highlighted and potential ways to overcome such are also discussed.

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“…Such a question still remains to be answered, and will be investigated in a future study. Nevertheless, our opinion, for the moment, especially based on nonlinear dynamic studies on streamflow (and other hydrologic data) and complex network studies on rainfall at different temporal scales, is that the streamflow network properties (including degree centrality, clustering coefficient, and degree distribution) may change for other temporal scales, despite the possible presence of scaling (or fractal) behavior in streamflow; see Sivakumar (2001), Sivakumar et al (2001Sivakumar et al ( , 2004Sivakumar et al ( , 2007, Regonda et al (2004), Salas et al (2005), Jha and Sivakumar (2017), and Naufan et al (2017) for some details. We hope to provide more reliable and convincing answers to this question in a future study, as we are currently conducting additional research on network properties in terms of scale and network size.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Temporal dynamics of streamflow: application of complex networks

et al. 2018

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This study employs the concepts of complex networks to study the temporal dynamics of streamflow, with emphasis on annual scale (i.e., year-to-year connections). The study proposes a new approach to construct the streamflow network at the annual scale. It uses the daily streamflow data to construct the annual streamflow network, instead of using the annual (mean or accumulated) streamflow data. With this approach, each year serves as a node in the network, with each node having a time series of daily streamflow values (not a single streamflow value). Streamflow data observed over a period of 151 years (October 1862-September 2013) from the Mississippi River basin at St. Louis, Missouri, USA are considered for implementation of the approach. The properties of the annual streamflow network are investigated using three complex network-based methods: degree centrality, clustering coefficient, and degree distribution. The sensitivity of the results to streamflow correlation threshold is also examined. The results suggest that (1) there are only a few very significant nodes (years) in the annual streamflow network (degree centrality method); (2) the annual streamflow network is not a classical random graph, but may be a small-world network or scale-free network (clustering coefficient method); and (3) the network exhibits a combination of exponential and power-law distribution (degree distribution method). Based on the identification of a significant stretch of period (around the 1950s-1990s) with very weak connections with the rest of the period studied, the results also suggest the influence of dam construction (and other anthropogenic factors) on the evolution of annual streamflow dynamics.

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Streamflow disaggregation: a nonlinear deterministic approach

Cited by 21 publications

References 37 publications

Nonlinear dynamics and chaos in hydrologic systems: latest developments and a look forward

Nonlinear dynamics and chaos in hydrologic systems: latest developments and a look forward

Dominant processes concept, model simplification and classification framework in catchment hydrology

Temporal dynamics of streamflow: application of complex networks

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