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Volume 22, issue 8 | Copyright
Hydrol. Earth Syst. Sci., 22, 4593-4604, 2018
https://doi.org/10.5194/hess-22-4593-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 30 Aug 2018

Research article | 30 Aug 2018

How good are hydrological models for gap-filling streamflow data?

Yongqiang Zhang and David Post Yongqiang Zhang and David Post
  • CSIRO Land and Water, GPO Box 1700, Acton 2601, Canberra, Australia

Abstract. Gap-filling streamflow data is a critical step for most hydrological studies, such as streamflow trend, flood, and drought analysis and hydrological response variable estimates and predictions. However, there is a lack of quantitative evaluation of the gap-filled data accuracy in most hydrological studies. Here we show that when the missing data rate is less than 10%, the gap-filled streamflow data obtained using calibrated hydrological models perform almost the same as the benchmark data (less than 1% missing) when estimating annual trends for 217 unregulated catchments widely spread across Australia. Furthermore, the relative streamflow trend bias caused by the gap filling is not very large in very dry catchments where the hydrological model calibration is normally poor. Our results clearly demonstrate that the gap filling using hydrological modelling has little impact on the estimation of annual streamflow and its trends.

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It is a critical step to gap-fill streamflow data for most hydrological studies, such as streamflow trend, flood, and drought analysis and predictions. However, quantitative evaluation of the gap-filled data accuracy is not available. Here we conducted the first comprehensive study, and found that when the missing data rate is less than 10 %, the gap-filled streamflow data using hydrological models are reliable for annual streamflow and its trend analysis.
It is a critical step to gap-fill streamflow data for most hydrological studies, such as...
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