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Evaluates uncertainty estimation and error correction in water resource models, suggesting improved predictions.
• A practical framework is developed to estimate uncertainty in water resource models. • The framework generates many model realisations using plausible flow perturbations. • The approach simultaneously corrects flow predictions and quantifies uncertainty. • The framework is tested using three candidate non-parametric approaches. • The k-means clustering approach demonstrated the best overall fit and reliability.
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Kim et al. (2026) studied this question.
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