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Hydrograph variances over different timescales in hydropower production networks
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Hydraulic Engineering.ORCID iD: 0000-0003-4036-424X
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Hydraulic Engineering.
2016 (English)In: Water resources research, ISSN 0043-1397, E-ISSN 1944-7973, Vol. 52, no 8, 5829-5846 p.Article in journal (Refereed) Published
Abstract [en]

The operation of water reservoirs involves a spectrum of timescales based on the distribution of stream flow travel times between reservoirs, as well as the technical, environmental, and social constraints imposed on the operation. In this research, a hydrodynamically based description of the flow between hydropower stations was implemented to study the relative importance of wave diffusion on the spectrum of hydrograph variance in a regulated watershed. Using spectral decomposition of the effluence hydrograph of a watershed, an exact expression of the variance in the outflow response was derived, as a function of the trends of hydraulic and geomorphologic dispersion and management of production and reservoirs. We show that the power spectra of involved time-series follow nearly fractal patterns, which facilitates examination of the relative importance of wave diffusion and possible changes in production demand on the outflow spectrum. The exact spectral solution can also identify statistical bounds of future demand patterns due to limitations in storage capacity. The impact of the hydraulic description of the stream flow on the reservoir discharge was examined for a given power demand in River Dalälven, Sweden, as function of a stream flow Peclet number. The regulation of hydropower production on the River Dalälven generally increased the short-term variance in the effluence hydrograph, whereas wave diffusion decreased the short-term variance over periods of <1 week, depending on the Peclet number (Pe) of the stream reach. This implies that flow variance becomes more erratic (closer to white noise) as a result of current production objectives.

Place, publisher, year, edition, pages
Blackwell Publishing, 2016. Vol. 52, no 8, 5829-5846 p.
Keyword [en]
diffusion model, hydropower, operation management, watershed dynamics, Computer hardware description languages, Diffusion, Hydroelectric power, Reservoirs (water), Rivers, Stream flow, Structures (built objects), Travel time, Watersheds, White noise, Hydro power production, Hydropower stations, Social constraints, Spectral decomposition, Statistical bounds, Peclet number
National Category
Geophysics
Identifiers
URN: urn:nbn:se:kth:diva-194995DOI: 10.1002/2015WR017775ScopusID: 2-s2.0-84987994865OAI: oai:DiVA.org:kth-194995DiVA: diva2:1044215
Note

QC 20161102

Available from: 2016-11-02 Created: 2016-11-01 Last updated: 2016-11-02Bibliographically approved

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Zmijewski, NicholasWörman, Anders
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