Qualitative and quantitative uncertainties in regional rainfall frequency analysis
Zhu et al. / J Zhejiang Univ-Sci A (Appl Phys & Eng)
1673-565X
Qualitative and quantitative uncertainties in regional rainfall frequency analysis*
Qian ZHU 0
Xiao XU 0
Chao GAO 0
Qi-hua RAN 0
Yue-ping XU 0
0 (Institute of Hydrology and Water Resources, Zhejiang University , Hangzhou 310058 , China)
Uncertainty exists widely in hydrological analysis, and this makes the process of uncertainty assessment very important for making robust decisions. In this study, uncertainty sources in regional rainfall frequency analysis are identified for the first time. The numeral unite spread assessment pedigree (NUSAP) method is introduced and is first employed to quantify qualitative uncertainty in regional rainfall frequency analysis. A pedigree matrix is particularly designed for regional rainfall frequency analysis, by which the qualitative uncertainty can be quantified. Finally, the qualitative and quantitative uncertainties are combined in an uncertainty diagnostic diagram, which makes the uncertainty evaluation results more intuitive. From the integrated diagnostic diagram, it can be determined that the uncertainty caused by the precipitation data is the smallest, and the uncertainty from different grouping methods is the largest. For the downstream sub-region, a generalized extreme value (GEV) distribution is better than a generalized logistic (GLO) distribution; for the south sub-region, a Pearson type III (PE3) distribution is the better choice; and for the north sub-region, GEV is more appropriate.
Qualitative uncertainty; Uncertainty analysis; Numeral unite spread assessment pedigree (NUSAP) method; Regional rainfall frequency analysis; Pedigree matrix; Diagnostic diagram doi; 10; 1631/jzus; A1400123 Document code; A CLC number; TV125
-
Regional frequency analysis is an important
topic in hydrology and water resources. However,
uncertainties existing in regional frequency analysis
make this problem complicated. Statistical flood or
rainfall estimates are affected by increasing
uncertainty with decreasing frequency of occurrence
because the quantiles of the probability distribution of
the extreme flood flows or rainfall amounts are
inferred from a data sample of relatively short length
(Michele and Rosso, 2001). Regionalization
procedures attempt to overcome the shortage of limited
measurement data through increasing the sample size
by substituting ‘space’ to augment ‘time’. However,
besides the data sources, a lot of other uncertainties
exist in regional frequency analysis. There is
uncertainty from different grouping methods to define the
hydrological homogeneous regions. The choice of
frequency distribution is also an uncertainty source
for regional frequency analysis. Thus, evaluating the
uncertainty in regional frequency analysis is
important, particularly for robust engineering
infrastructure design and management.
Mainstream methods, such as generalized
likelihood uncertainty estimation (GLUE) (Jin et al.,
2010; Li et al., 2010; Delsman et al., 2013), Monte
Caro simulation (Jeremiah et al., 2011; Houska et al.,
2013), and the Bayesian approach (Parent and Bernier,
2003; Reis and Stedinger, 2005; Bouda et al., 2012),
are often used for uncertainty analysis. Key
dimensions of uncertainty in regional frequency
analysis that need to be addressed are technical,
methodological, and epistemological. Quantitative
methods mentioned above address the technical
dimension only (van der Sluijs et al., 2005). Qualitative
uncertainties, such as those originating from methods,
are far less well studied. Therefore, how to quantify
qualitative uncertainty remains a difficult task in
hydrological analysis. Apart from that, how to assess the
qualitative and quantitative uncertainties integrally is
also a great challenge.
In this paper, to account for the qualitative
uncertainty, the numeral unite spread assessment
pedigree (NUSAP) method proposed by Funtowicz and
Ravetz (1990) is introduced for the first time to
evaluate both quantitative and qualitative
uncertainties in regional rainfall frequency analysis (RFA). The
NUSAP method is able to address aspects of data,
methods, or model quality resulting from
uncertainties that are hard to quantify, such as methodological
and epistemological uncertainties, and that are not
systematically taken into account in scientific studies.
A pedigree matrix is particularly designed for
regional rainfall frequency analysis, by which the
qualitative uncertainty can be effectively quantified.
2 Methodology
The framework of this study is presented in
Fig. 1. Uncertainty sources in regional frequency
analysis are first defined. Among these, three sources,
i.e., precipitation measurement error, different
methods to identify homogeneous regions, and
different frequency distributions, are selected to assess
their impact on the quantitative and qualitative
uncertainties on design rainfall with the NUSAP
method. The pedigree matrix, particularly designed for
regional frequency analysis, i (...truncated)