Abstract
Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.
Highlights
Hydrological models are developed to understand process, test hypothesis, and support decision-making
Model development has progressed in accounting for different processes and complexities, they remain as simplifications of the actual hydrological processes
We have found six broad classes of uncertainty analysis (UA) methods: (i) Monte Carlo sampling, (ii) response surface-based schemes including polynomial chaos expansion and machine learning, (iii) multi-modeling approaches, (iv) Bayesian statistics, (v) multi-objective analysis, and (vi) least square-based inverse modeling (Figure 2)
Summary
Hydrological models are developed to understand process, test hypothesis, and support decision-making. A few of the methods include generalized likelihood uncertainty estimation (GLUE) [17], differential evolution adaptive metropolis (DREAM) [18], parameter estimation code (PEST) [19], Bayesian total error analysis (BATEA) [20], and multi-objective analysis (Borg) [21] These techniques have been applied in different water resources decision-making such as water supply design [22], flood mapping [23,24], and hydropower plant evaluation [25]. We newly reviewed response surface methods, uncertainty source interactions and provided a much-needed review of UA applications for hydrologic prediction in ungauged basins. These make our work a novel contribution that critiqued UA in both gauged and ungauged catchments
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