Abstract
Many cities face issues with rain induced flooding and combined sewer overflows, which can be addressed by using hydrodynamic models. These models are often simplified in a real-time setting to make them faster, and their performance can be improved by using data assimilation. In this study we use the Ensemble Kalman Filter to update a simplified model of a small area of Copenhagen, Denmark. The model is
evaluated using perfect rain data for one summer month in 2016, and flow forecasts are used to quantify the performance of the update. We found that the 1-60 minutes forecast can be improved by updating the model. Having a small noise on the rain gives slightly worse results on a short forecast horizon and slightly better forecasts on a longer horizon. The forecast performance is also dependent on which model parts are updated.
evaluated using perfect rain data for one summer month in 2016, and flow forecasts are used to quantify the performance of the update. We found that the 1-60 minutes forecast can be improved by updating the model. Having a small noise on the rain gives slightly worse results on a short forecast horizon and slightly better forecasts on a longer horizon. The forecast performance is also dependent on which model parts are updated.
Original language | English |
---|---|
Title of host publication | Proceedings of the 11th International Conference on Urban Drainage Modelling |
Editors | G. Mannina |
Place of Publication | Palermo, Italy |
Publication date | 2018 |
Pages | 710-714 |
Publication status | Published - 2018 |
Event | 11th International Conference on Urban Drainage Modelling - Palermo, Italy Duration: 23 Sept 2018 → 26 Sept 2018 |
Conference
Conference | 11th International Conference on Urban Drainage Modelling |
---|---|
Country/Territory | Italy |
City | Palermo |
Period | 23/09/2018 → 26/09/2018 |
Keywords
- Data assimilation
- Surrogate model
- Flow forecasting