Estimating the uncertainty of hydrological predictions through data-driven resampling techniques

A. Sikorska, A. Montanari, and D. Koutsoyiannis, Estimating the uncertainty of hydrological predictions through data-driven resampling techniques, Journal of Hydrologic Engineering (ASCE), 20 (1), doi:10.1061/(ASCE)HE.1943-5584.0000926, 2015.



Estimating the uncertainty of hydrological models remains a relevant challenge in applied hydrology, mostly because it is not easy to parameterize the complex structure of hydrological model errors. A non-parametric technique is proposed as an alternative to parametric error models to estimate the uncertainty of hydrological predictions. Within this approach, the above uncertainty is assumed to depend on input data uncertainty, parameter uncertainty and model error, where the latter aggregates all sources of uncertainty that are not considered explicitly. Errors of hydrological models are simulated by resampling from their past realizations using a nearest neighbor approach, therefore avoiding a formal description of their statistical properties. The approach is tested using synthetic data which refer to the case study located in Italy. The results are compared with those obtained using a formal statistical technique (meta-Gaussian approach) from the same case study. Our findings prove that the nearest neighbor approach provides simplicity in application and a significant improvement in regard to the meta-Gaussian approach. Resampling techniques appear therefore to be an interesting option for uncertainty assessment in hydrology, provided that historical data are available to provide a consistent description of the model error.

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Our works referenced by this work:

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Our works that reference this work:

1. A. Montanari, and D. Koutsoyiannis, Reply to comment by G. Nearing on ‘‘A blueprint for process-based modeling of uncertain hydrological systems’’, Water Resources Research, 50 (7), 6264–6268, doi:10.1002/2013WR014987, 2014.
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5. D. Koutsoyiannis, and A. Montanari, Bluecat: A local uncertainty estimator for deterministic simulations and predictions, Water Resources Research, 58 (1), e2021WR031215, doi:10.1029/2021WR031215, 2022.
6. D. Koutsoyiannis, and A. Montanari, Climate extrapolations in hydrology: The expanded Bluecat methodology, Hydrology, 9, 86, doi:10.3390/hydrology9050086, 2022.
7. E. Rozos, D. Koutsoyiannis, and A. Montanari, KNN vs. Bluecat — Machine Learning vs. Classical Statistics, Hydrology, 9, 101, doi:10.3390/hydrology9060101, 2022.
8. D. Koutsoyiannis, Stochastics of Hydroclimatic Extremes - A Cool Look at Risk, Edition 3, ISBN: 978-618-85370-0-2, 391 pages, doi:10.57713/kallipos-1, Kallipos Open Academic Editions, Athens, 2023.

Works that cite this document: View on Google Scholar or ResearchGate

Other works that reference this work (this list might be obsolete):

1. Thompson, S. E., M. Sivapalan, C. J. Harman, V. Srinivasan, M. R. Hipsey, P. Reed, A. Montanari and G. and Blöschl, Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene, Hydrol. Earth Syst. Sci., 17, 5013-5039, 2013.
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3. Vogel, M., Stochastic watershed models for hydrologic risk management, Water Security, doi:10.1016/j.wasec.2017.06.001, 2017.

Tagged under: Determinism vs. stochasticity, Hydrological models, Uncertainty