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.

[doc_id=1414]

[English]

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.

Full text is only available to the NTUA network due to copyright restrictions

PDF Additional material:

See also: http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000926

Our works referenced by this work:

1. D. Koutsoyiannis, H. Yao, and A. Georgakakos, Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods, Hydrological Sciences Journal, 53 (1), 142–164, doi:10.1623/hysj.53.1.142, 2008.
2. A. Montanari, and D. Koutsoyiannis, A blueprint for process-based modeling of uncertain hydrological systems, Water Resources Research, 48, W09555, doi:10.1029/2011WR011412, 2012.
3. D. Koutsoyiannis, Hydrology and Change, Hydrological Sciences Journal, 58 (6), 1177–1197, doi:10.1080/02626667.2013.804626, 2013.
4. A. Montanari, G. Young, H. H. G. Savenije, D. Hughes, T. Wagener, L. L. Ren, D. Koutsoyiannis, C. Cudennec, E. Toth, S. Grimaldi, G. Blöschl, M. Sivapalan, K. Beven, H. Gupta, M. Hipsey, B. Schaefli, B. Arheimer, E. Boegh, S. J. Schymanski, G. Di Baldassarre, B. Yu, P. Hubert, Y. Huang, A. Schumann, D. Post, V. Srinivasan, C. Harman, S. Thompson, M. Rogger, A. Viglione, H. McMillan, G. Characklis, Z. Pang, and V. Belyaev, “Panta Rhei – Everything Flows”, Change in Hydrology and Society – The IAHS Scientific Decade 2013-2022, Hydrological Sciences Journal, 58 (6), 1256–1275, doi:10.1080/02626667.2013.809088, 2013.

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.
2. S. Ceola, A. Montanari, and D. Koutsoyiannis, Toward a theoretical framework for integrated modeling of hydrological change, WIREs Water, 1 (5), 427–438, doi:10.1002/wat2.1038, 2014.
3. A. Efstratiadis, I. Nalbantis, and D. Koutsoyiannis, Hydrological modelling of temporally-varying catchments: Facets of change and the value of information, Hydrological Sciences Journal, 60 (7-8), 1438–1461, doi:10.1080/02626667.2014.982123, 2015.

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.
2. Sikorska, A.E., D. Del Giudice, K. Banasik, and J. Rieckermann, The value of streamflow data in improving TSS predictions - Bayesian multi-objective calibration, Journal of Hydrology, doi:10.1016/j.jhydrol.2015.09.051, 2015.
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