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Timo A. Räsänen

Researcher at Aalto University

Publications -  21
Citations -  1484

Timo A. Räsänen is an academic researcher from Aalto University. The author has contributed to research in topics: Hydropower & Hydrometeorology. The author has an hindex of 13, co-authored 21 publications receiving 1185 citations.

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Future changes in Mekong River hydrology: impact of climate change and reservoir operation on discharge

TL;DR: In this paper, the authors assess the cumulative impact of climate change and reservoir operation on the hydrology of the transboundary Mekong River within the next 20-30 years.
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Designing river flows to improve food security futures in the Lower Mekong Basin

TL;DR: It is hypothesized that high fisheries yields are driven by measurable attributes of hydrologic variability, and that these relationships can be used to design and implement future flow regimes that improve fisheries yield through control of impending hydropower operations.
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Observed river discharge changes due to hydropower operations in the Upper Mekong Basin

TL;DR: In this paper, the authors assess the discharge changes using observed river discharge data and a distributed hydrological model over the period of 1960-2014 and find that the hydropower operations have considerably modified the river discharges since 2011 and the largest changes were observed in 2014.
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Downstream Hydrological Impacts of Hydropower Development in the Upper Mekong Basin

TL;DR: In this paper, the authors assess the impact of the Lancang-Jiang cascade on downstream hydrology by using a combination of a hydrological model and a reservoir cascade optimization model, and quantified in detail at the Chiang Saen gauging station in Thailand, the first gauge station downstream from the cascade, and in lesser detail at four downstream locations in the Mekong mainstream.
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Forest Loss in Protected Areas and Intact Forest Landscapes: A Global Analysis

TL;DR: In this article, the authors conducted a consistent assessment of the global forest loss in protected areas and IFLs over the period 2000-2012, using recently published global remote sensing based spatial forest cover change data, being a uniform and consistent dataset over space and time.