DOI: 10.5194/hess-24-2061-2020
论文题名: A new uncertainty estimation approach with multiple datasets and implementation for various precipitation products
作者: Zhou X. ; Polcher J. ; Yang T. ; Huang C.-S.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期: 4 起始页码: 2061
结束页码: 2081
语种: 英语
Scopus关键词: Gages
; Large dataset
; Plasma interactions
; Reactor cores
; Comprehensive assessment
; Precipitation products
; Sources of uncertainty
; Spatio-temporal dimensions
; Spatio-temporal scale
; Temporal and spatial scale
; Uncertainty estimates
; Uncertainty estimation
; Uncertainty analysis
; climate change
; data set
; estimation method
; precipitation (climatology)
; uncertainty analysis
英文摘要: Ensemble estimates based on multiple datasets are frequently applied once many datasets are available for the same climatic variable. An uncertainty estimate based on the difference between the ensemble datasets is always provided along with the ensemble mean estimate to show to what extent the ensemble members are consistent with each other. However, one fundamental flaw of classic uncertainty estimates is that only the uncertainty in one dimension (either the temporal variability or the spatial heterogeneity) can be considered, whereas the variation along the other dimension is dismissed due to limitations in algorithms for classic uncertainty estimates, resulting in an incomplete assessment of the uncertainties. This study introduces a three-dimensional variance partitioning approach and proposes a new uncertainty estimation (Ue) that includes the data uncertainties in both spatiotemporal scales. The new approach avoids pre-averaging in either of the spatiotemporal dimensions and, as a result, the Ue estimate is around 20 % higher than the classic uncertainty metrics. The deviation of Ue from the classic metrics is apparent for regions with strong spatial heterogeneity and where the variations significantly differ in temporal and spatial scales. This shows that classic metrics underestimate the uncertainty through averaging, which means a loss of information in the variations across spatiotemporal scales. Decomposing the formula for Ue shows that Ue has integrated four different variations across the ensemble dataset members, while only two of the components are represented in the classic uncertainty estimates. This analysis of the decomposition explains the correlation as well as the differences between the newly proposed Ue and the two classic uncertainty metrics. The new approach is implemented and analysed with multiple precipitation products of different types (e.g. gauge-based products, merged products and GCMs) which contain different sources of uncertainties with different magnitudes. Ue of the gaugebased precipitation products is the smallest, while Ue of the other products is generally larger because other uncertainty sources are included and the constraints of the observations are not as strong as in gauge-based products. This new threedimensional approach is flexible in its structure and particularly suitable for a comprehensive assessment of multiple datasets over large regions within any given period. © 2020 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162719
Appears in Collections: 气候变化与战略
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作者单位: Zhou, X., State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Center for Global Change and Water Cycle, Hohai University, Nanjing, 210098, China, Laboratoire Météorologie Dynamique du CNRS, IPSL, CNRS, Paris, 91128, France, Institute of Industrial Science, University of Tokyo, Tokyo, 153-8505, Japan; Polcher, J., Laboratoire Météorologie Dynamique du CNRS, IPSL, CNRS, Paris, 91128, France; Yang, T., State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Center for Global Change and Water Cycle, Hohai University, Nanjing, 210098, China; Huang, C.-S., State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Center for Global Change and Water Cycle, Hohai University, Nanjing, 210098, China
Recommended Citation:
Zhou X.,Polcher J.,Yang T.,et al. A new uncertainty estimation approach with multiple datasets and implementation for various precipitation products[J]. Hydrology and Earth System Sciences,2020-01-01,24(4)