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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 16, issue 4
Hydrol. Earth Syst. Sci., 16, 1221–1236, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 16, 1221–1236, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Apr 2012

Research article | 12 Apr 2012

Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models

A. E. Sikorska1,2, A. Scheidegger1, K. Banasik2, and J. Rieckermann1 A. E. Sikorska et al.
  • 1Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dept. of Urban Water Management, Überlandstrasse 133, Dübendorf, 8600, Switzerland
  • 2Warsaw University of Life Sciences – SGGW, Dept. of Hydraulic Engineering, 166 Nowoursynowska Street Warsaw, 02-787, Poland

Abstract. Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.

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