V
Vladimir Smakhtin
Researcher at International Water Management Institute
Publications - 168
Citations - 8744
Vladimir Smakhtin is an academic researcher from International Water Management Institute. The author has contributed to research in topics: Water resources & Drainage basin. The author has an hindex of 41, co-authored 167 publications receiving 7374 citations. Previous affiliations of Vladimir Smakhtin include Council of Scientific and Industrial Research & CGIAR.
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Journal ArticleDOI
Low flow hydrology: a review
TL;DR: Low-flow hydrology is a discipline which deals with minimum flow in a river during the dry periods of the year as mentioned in this paper, and it has been extensively studied in the literature.
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The state of desalination and brine production: A global outlook.
Edward R. Jones,Edward R. Jones,Manzoor Qadir,Michelle T. H. van Vliet,Vladimir Smakhtin,Seong Mu Kang,Seong Mu Kang +6 more
TL;DR: Improved brine management strategies are required to limit the negative environmental impacts and reduce the economic cost of disposal, thereby stimulating further developments in desalination facilities to safeguard water supplies for current and future generations.
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Comparison of seven meteorological indices for drought monitoring in Iran
TL;DR: In this paper, the performance of seven drought indices for monitoring in the Tehran province of Iran is compared, including deciles index (DI), percent of normal (PN), standard precipitation index (SPI), China-Z index (CZI), modified CZI (MCZI), Z-score and effective drought index (EDI).
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A Pilot Global Assessment of Environmental Water Requirements and Scarcity
TL;DR: In this paper, a first attempt to estimate the volume of water required for the maintenance of freshwater-dependent ecosystems at the global scale is presented, which consists of ecologically relevant low-flow and high-flow components and depends upon the objective of environmental water management.
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Drought forecasting using artificial neural networks and time series of drought indices
TL;DR: In this article, the authors used Artificial Neural Network (ANN) to predict quantitative values of drought indices, which are continuous functions of rainfall which measure the degree of dryness of any time period.