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Discussion papers
https://doi.org/10.5194/hess-2019-49
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-49
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Feb 2019

Research article | 05 Feb 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

A new uncertainty estimation technique for multiple datasets and its application to various precipitation products

Xudong Zhou1,2, Jan Polcher2, Tao Yang1, and Ching-Sheng Huang1 Xudong Zhou et al.
  • 1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Center for Global Change and Water Cycle, Hohai University, Nanjing 210098, China
  • 2Laboratoire Météorologie Dynamique du CNRS, IPSL, CNRS, Paris, F 91128, France

Abstract. The uncertainty among climatological datasets can be characterized as the variance in space and time between various estimates of the same quantity. However, some of the current uncertainty estimates only evaluate variations in one single dimension (time or space) due to the limitation of estimation methodology as averaging variation is necessary for the other dimension. The influence on the uncertainty assessment of the ignorance of variations in one dimension is not well studied. This study introduces a new three-dimensional variance partitioning approach which avoids the averaging and provides an new uncertainty estimation (Ue) technique with consideration of both temporal and spatial variations. Comparisons of Ue to classic uncertainty estimations show that the classic metrics underestimate the uncertainty because of the averaging of variation in the time or space dimension, and Ue is around 20 % higher than classic estimations. The deviation between the new and classic metrics is higher for regions with strong spatial heterogeneity and where the spatial and temporal variations significantly differ. Decomposing of the new metric demonstrates that Ue is a comprehensive assessment of model uncertainty which has been included the model variations identified by the classic metrics. Multiple precipitation products of different types (gauge-based, merged products and GCMs) are used to better explain and understand the peculiarity of the new methodology. The new uncertainty estimation technique is flexible in its structure and particularly suitable for a comprehensive assessment of multiple datasets over a large regions within any given period.

Xudong Zhou et al.
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Short summary
This article proposes a new uncertainty estimation technique for multiple datasets. The new uncertainty estimation method considers all the variations in time and space dimensions, so that it avoids the averaging variation in either of the dimension which is necessary in classic uncertainty estimations. Comparisons with classic and new uncertainty metrics demonstrate that classic metrics may underestimate the uncertainties among datasets.
This article proposes a new uncertainty estimation technique for multiple datasets. The new...
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