Abstract
The absence of aggregated uncertainty measures restricts the assessment of uncertainty in hydrological simulation. In this work, a new composite uncertainty measure is developed to evaluate the complex behaviors of uncertainty existing in hydrological simulation. The composite uncertainty measure is constructed based on a framework, which includes three steps: (1) identification of behavioral measures by analyzing the pairwise correlations among different measures and removing high correlations; (2) weight assignment by means of a new hierarchical weight assembly (HWA) approach incorporating the intra-class and inter-class weights; (3) construction of a composite uncertainty measure through incorporating multiple properties of the measure matrix. The framework and the composite uncertainty measure are demonstrated by case studies in uncertainty assessment for hydrological simulation. Results indicate that the framework is efficient to generate a composite uncertainty index (denoted as CUI) and the new measure CUI is competent for uncertainty evaluation. Besides, the HWA approach performs well in weighting, which can characterize subjective and objective properties of the information matrix. The achievement of this work provides promising insights into the performance comparison of uncertainty analysis approaches, the selection of proper cut-off threshold in the GLUE method, and the guidance of reasonable uncertainty assessment in a range of environmental modelling.
Highlights
Uncertainty is a term used across many disciplines without identical definitions, but in essence it is related to the variable associated with the difference between the true state of a system and its observational or theoretical assessment at a specific space and time [1]
We strive to: (1) construct a consolidated framework to generate a new composite uncertainty measure to evaluate the uncertainty of hydrological simulation; (2) develop an integrated weighting approach based on a hierarchical ensemble of multiple matrix properties incorporating the intra-class and inter-class weights; and (3) develop a composite uncertainty index (CUI) by incorporating a range of uncertainty measures and well-assessed weights
Considering that the SCEM-UA method with a standard likelihood measure usually generates too small an interval to cover observed points [26], we adopt the same handlings as Blasone [26], where the likelihood measure of the SCEM-UA method is changed to the Nash-Sutcliffe Coefficient of Efficiency (NSCE): n i=1 (Obsi − Simi )2 (Obsi − Obs) where, Obsi and Simi are respectively the observation and simulation at ith time step, Obs is the mean of observations; n is the length of discharge series
Summary
Uncertainty is a term used across many disciplines without identical definitions, but in essence it is related to the variable associated with the difference between the true state of a system and its observational or theoretical assessment at a specific space and time [1]. It follows that their uncertainty assessment indices must reflect such uncertainties In these uncertainty assessment methods, the output of the hydrological modelling exercise for each time step is no longer a single numerical value (point estimate) of discharge or stage, but rather an interval defined by the prediction bounds obtained under a certain confidence level. An MADM method is a procedure that specifies how attribute information is to be processed in order to arrive at a choice [36] It is a method of assembling behavioral measures by well-assessed weights. We strive to: (1) construct a consolidated framework to generate a new composite uncertainty measure to evaluate the uncertainty of hydrological simulation; (2) develop an integrated weighting approach based on a hierarchical ensemble of multiple matrix properties incorporating the intra-class and inter-class weights; and (3) develop a composite uncertainty index (CUI) by incorporating a range of uncertainty measures and well-assessed weights. This study provides a new approach to evaluate the uncertainty inherently existing in hydrological simulation
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