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dc.contributor.authorChavan, S. R.-
dc.contributor.authorSrinivas, V. V.-
dc.date.accessioned2021-07-21T21:24:51Z-
dc.date.available2021-07-21T21:24:51Z-
dc.date.issued2021-07-22-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/2161-
dc.description.abstractEstimates of probable maximum precipitation (PMP) and corresponding probable maximum flood (PMF) are necessary for planning, design, and risk assessment of flood control structures whose failure could have catastrophic consequences. For PMP estimation, multifractal approach (MA) is deemed to be better than conventional approaches, which are based either on statistical concepts or physical aspects. The MA yields physically meaningful PMP estimates by attempting to capture scale-invariant multiplicative cascade mechanism inherent in rainfall. This paper attempts to gain insights into the performance of MA by comparing PMP estimates obtained using the approach with those resulting from the use of two widely used empirical approaches (storm maximization approach (SMA) and Hershfield method (HM)) on two flood-prone river basins (Mahanadi and Godavari) in India. The results indicate that rainfall data of the two river basins exhibit multifractal properties, and the use of MA has an advantage over HM and SMA in estimating PMP corresponding to longer durations (>3 days). PMP estimates obtained using HM are generally lower (higher) than those obtained using SMA for 1-day (higher) duration. PMP maps are prepared for the two Indian river basins corresponding to 1-day to 5-day durations. Further, PMP estimates obtained based on the PMP maps are provided for 18 catchments in the Mahanadi basin and 53 catchments in the Godavari river basin.en_US
dc.language.isoen_USen_US
dc.subjectProbable maximum precipitationen_US
dc.subjectStorm maximization approachen_US
dc.subjectHershfield methoden_US
dc.subjectMultifractal approachen_US
dc.subjectIndiaen_US
dc.titleEvaluation of three approaches to probable maximum precipitation estimation: a study on two Indian river basinsen_US
dc.typeArticleen_US
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