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High temporal resolution rainfall rate estimation from rain gauge measurements

Research output: Contribution to journalArticle

Original languageEnglish
Article numberjh2017054
Number of pages12
JournalJournal of Hydroinformatics
Early online date24 Aug 2017
DateAccepted/In press - 22 Jun 2017
DateE-pub ahead of print (current) - 24 Aug 2017


Rainfall rates derived from “Tipping-Bucket rain gauges” generally ignore the detailed variation at a finer temporal scale that particularly occurs in light rainfall events. This study extends the exploration of using artificial neural networks (ANNs), in comparison with the conventional Linear Interpolation Method (LIM) and the Cubic Spline Algorithm (CSA) for rainfall rate estimation at fine temporal resolution using rain gauge data based on a case study at Chilbolton and Sparsholt Observatories, U.K. A supervised feed-forward neural network integrated with the backpropagation algorithm is used to identify the complex nonlinear relationships between input and target variables. The results indicate that the ANN considerably outperforms the CSA and LIM with higher Nash-Sutcliffe Efficiency (NSE), lower Root Mean Square Error (RMSE) and lower rainfall amount differences when compared to the disdrometer observations when the model is trained within a broad span of input values. Consistent stability in accurately estimating rainfall rate in different sites shows the intrinsic advantage of ANNs in learning and self-adaptive abilities in modelling complex nonlinear relationships between the inputs and target variables.

    Research areas

  • ANN, CSA, Disdrometer, LIM, Rainfall rate/intensity, Tipping-Bucket rain gauge

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    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IWA Publishing at Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 1.22 MB, PDF document


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