Lookup NU author(s): Professor Hayley Fowler,
Dr Sean Wilkinson
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2017 Elsevier B.V. For a few decades the local rainfall measurements are generally obtained by tipping bucket sensors, that allow to record each tipping time corresponding to a well-known rain depth. However, a considerable part of rainfall data to be used in the hydrological practice is available in aggregated form within constant time intervals. This can produce undesirable effects, like the underestimation of the annual maximum rainfall depth, Hd, associated with a given duration, d, that is the basic quantity in the development of rainfall depth-duration-frequency relationships. The errors in the evaluation of Hd from data characterized by a coarse temporal aggregation, ta, and a procedure to reduce the non-homogeneity of the Hd series are here investigated. Our results show that for ta = 1 min the underestimation is practically negligible, whereas for larger temporal aggregations with d = ta the error in a single Hd can reach values up to 50% and in a series of Hd in the average up to 17%. Relationships between the non-dimensional ratio ta/d and the average underestimation of Hd, derived through continuous rainfall data observed in many stations of Central Italy, are presented to overcome this issue. These equations allow to improve the Hd estimates and the associated depth-duration-frequency curves at least in areas with similar climatic conditions. The effect of the correction of the Hd series on the rainfall depth-duration-frequency curves is quantified. Our results indicate that the improvements obtained by the proposed procedure are of the order of 10%.
Author(s): Morbidelli R, Saltalippi C, Flammini A, Cifrodelli M, Picciafuoco T, Corradini C, Casas-Castillo MC, Fowler HJ, Wilkinson SM
Publication type: Article
Publication status: Published
Journal: Journal of Hydrology
Print publication date: 01/11/2017
Online publication date: 28/09/2017
Acceptance date: 27/09/2017
ISSN (print): 0022-1694
Publisher: Elsevier B.V.
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