A data-based probabilistic approach to calculate and visualise the uncertainty of flood forecasts
Van Steenbergen, N.; Ronsyn, J.; Willems, P.; Van Eerdenbrugh, K. (2011). A data-based probabilistic approach to calculate and visualise the uncertainty of flood forecasts, in: Zenz, G. et al. (Ed.) Proceedings of the International Symposium UFRIM - Urban Flood Risk Management: approaches to enhance resilience of communities, 21st-23rd September 2011, Graz, Austria. pp. [1-6]
In: Zenz, G.; Hornich, R. (Ed.) (2011). Proceedings of the International Symposium UFRIM - Urban Flood Risk Management: approaches to enhance resilience of communities, 21st-23rd September 2011, Graz, Austria. Graz University of Technology. Verlag der Technischen Universität Graz: Graz. ISBN 978-3-85125-173-9. XVIII, 593 pp., meer
|
| Beschikbaar in | Auteurs |
|
Documenttype: Congresbijdrage
|
| Trefwoorden |
Error analysis
Prediction > Flood forecasting
Uncertainty
|
| Auteurs | | Top |
- Van Steenbergen, N.
- Ronsyn, J., meer
- Willems, P., meer
- Van Eerdenbrugh, K., meer
|
|
|
| Abstract |
The authorities of the Flanders region of Belgium have set up several flood forecasting systems to be used as early warning systems. These flood forecasting systems make use of hydrological and hydrodynamic submodels and rainfall forecasts. Each of these models and forecasts is subject to significant uncertainties, which accumulate to an uncertainty in the forecast results. A method is set up to calculate and visualise this uncertainty as well as the exceedance probability of alert and alarm levels.
The method consists of an error analysis on model simulation results for historical periods. The model residuals have been statistically analysed using a non parametric technique. Because the residuals depend on the value of the simulated water level and the time horizon, the residuals are split up into discrete value classes, based on the simulated water levels and different time horizons. Percentile values are calculated for the residuals and stored in a three dimensional error matrix. Based on a 3D interpolation in the error matrix, a bias correction is executed and confidence intervals on simulation results are calculated and visualised. The exceedance probabilities of alert and alarm levels are calculated and used to provide probabilistic information on the forecasts to water managers. |
|
