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Value of different precipitation data for flood prediction in an alpine catchment: A Bayesian approach


Sikorska, Anna E; Seibert, Jan (2016). Value of different precipitation data for flood prediction in an alpine catchment: A Bayesian approach. Journal of Hydrology:Epub ahead of print.

Abstract

Flooding induced by heavy precipitation is one of the most severe natural hazards in alpine catchments. To accurately predict such events, accurate and representative precipitation data are required. Estimating catchment precipitation is, however, difficult due to its high spatial, and, in the mountains, elevation-dependent variability. These inaccuracies, together with runoff model limitations, translate into uncertainty in runoff estimates. Thus, in this study, we investigate the value of three precipitation datasets, commonly used in hydrological studies, i.e., station network precipitation (SNP), interpolated grid precipitation (IGP) and radar-based precipitation (RBP), for flood predictions in an alpine catchment. To quantify their effects on runoff simulations, we perform a Bayesian uncertainty analysis with an improved description of model systematic errors. By using periods of different lengths for model calibration, we explore the information content of these three datasets for runoff predictions. Our results from an alpine catchment showed that using SNP resulted in the largest predictive uncertainty and the lowest model performance evaluated by the Nash–Sutcliffe efficiency. This performance improved from 0.674 to 0.774 with IGP, and to 0.829 with RBP. The latter two datasets were also much more informative than SNP, as half as many calibration data points were required to obtain a good model performance. Thus, our results show that the various types of precipitation data differ in their value for flood predictions in an alpine catchment and indicate RBP as the most useful dataset.

Abstract

Flooding induced by heavy precipitation is one of the most severe natural hazards in alpine catchments. To accurately predict such events, accurate and representative precipitation data are required. Estimating catchment precipitation is, however, difficult due to its high spatial, and, in the mountains, elevation-dependent variability. These inaccuracies, together with runoff model limitations, translate into uncertainty in runoff estimates. Thus, in this study, we investigate the value of three precipitation datasets, commonly used in hydrological studies, i.e., station network precipitation (SNP), interpolated grid precipitation (IGP) and radar-based precipitation (RBP), for flood predictions in an alpine catchment. To quantify their effects on runoff simulations, we perform a Bayesian uncertainty analysis with an improved description of model systematic errors. By using periods of different lengths for model calibration, we explore the information content of these three datasets for runoff predictions. Our results from an alpine catchment showed that using SNP resulted in the largest predictive uncertainty and the lowest model performance evaluated by the Nash–Sutcliffe efficiency. This performance improved from 0.674 to 0.774 with IGP, and to 0.829 with RBP. The latter two datasets were also much more informative than SNP, as half as many calibration data points were required to obtain a good model performance. Thus, our results show that the various types of precipitation data differ in their value for flood predictions in an alpine catchment and indicate RBP as the most useful dataset.

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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:2016
Deposited On:01 Jul 2016 12:35
Last Modified:01 Jul 2016 12:35
Publisher:Elsevier
ISSN:0022-1694
Publisher DOI:https://doi.org/10.1016/j.jhydrol.2016.06.031

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