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. (C) 2016 Elsevier B.V. All rights reserved.

Keywords

Precipitation uncertainty; Station network precipitation; Interpolated grid precipitation; Radar-based precipitation; Flood prediction; Bayesian approach

Published in

Journal of Hydrology
2018, Volume: 556, pages: 961-971
Publisher: ELSEVIER SCIENCE BV

UKÄ Subject classification

Oceanography, Hydrology, Water Resources


Publication identifier

DOI: https://doi.org/10.1016/j.jhydrol.2016.06.031

Permanent link to this page (URI)

https://res.slu.se/id/publ/98787