Header

UZH-Logo

Maintenance Infos

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


Sikorska, Anna E; Montanari, Alberto; Koutsoyiannis, Demetris (2015). Estimating the uncertainty of hydrological predictions through data-driven resampling techniques. Journal of Hydrologic Engineering, 20(A4014009):online.

Abstract

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 nonparametric 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.

Abstract

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 nonparametric 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.

Statistics

Citations

8 citations in Web of Science®
6 citations in Scopus®
Google Scholar™

Altmetrics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2015
Deposited On:22 Jan 2015 16:08
Last Modified:05 Apr 2016 18:48
Publisher:American Society of Civil Engineers
ISSN:1084-0699
Additional Information:SPECIAL ISSUE: Grand Challenges in Hydrology
Publisher DOI:https://doi.org/10.1061/(ASCE)HE.1943-5584.0000926

Download

Full text not available from this repository.
View at publisher