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  • Effect of data length on rainfall-runoff modelling

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    Author(s)
    Boughton, Walter
    Griffith University Author(s)
    Boughton, Walter C.
    Year published
    2007
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    Abstract
    A 64-year data set of daily rainfall and runoff, and average monthly potential evapotranspiration (PET) was split into subsets of 2, 5, 10, 20 and 30 years. Each subset was used to calibrate the AWBM daily rainfall-runoff model. Each subset calibration was then used to estimate runoff from the 64 years of rainfall and PET data. The ratios of calculated to actual total runoff were used to determine the ranges of error from the different lengths of data used for calibration. There was little difference in results from the 2- and 5-year subsets with 90% of estimates of long term runoff in the range of -21% to +31% of the recorded ...
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    A 64-year data set of daily rainfall and runoff, and average monthly potential evapotranspiration (PET) was split into subsets of 2, 5, 10, 20 and 30 years. Each subset was used to calibrate the AWBM daily rainfall-runoff model. Each subset calibration was then used to estimate runoff from the 64 years of rainfall and PET data. The ratios of calculated to actual total runoff were used to determine the ranges of error from the different lengths of data used for calibration. There was little difference in results from the 2- and 5-year subsets with 90% of estimates of long term runoff in the range of -21% to +31% of the recorded value. Overestimation of long term runoff reduced with length of calibration data of 10 or more years; however, the chances of underestimating were only slightly reduced even with 30 years of calibration data. Some limited repetition of the calculations with the Curve Number rainfall-runoff model indicated that the error characteristics were inherent in the data set and not an artifact of the model used. The ramifications for applications of rainfall-runoff modelling are briefly discussed.
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    Journal Title
    Environmental Modelling & Software
    Volume
    22
    DOI
    https://doi.org/10.1016/j.envsoft.2006.01.001
    Copyright Statement
    © 2007 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
    Publication URI
    http://hdl.handle.net/10072/18951
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