Utility of different data types for calibrating flood inundation models within a GLUE framework
- 1School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK
- 2Department of Civil Engineering, University of Bristol, Queen's Building, University Walk, Bristol, BS8 1TR, UK
- 3European Commission, Joint Research Centre, Institute for Environment and Sustainability (IES), Weather Driven Natural Hazards Action; LM Unit, Via E. Fermi, TP 261, 21020 Ispra (Va), Italy
- 4WL/Delft Hydraulics, P.O. Box 5048, 2600 GA, Delft, The Netherlands
- Email for corresponding author: Neil.Hunter@bristol.ac.uk
Abstract. To translate a point hydrograph forecast into products for use by environmental agencies and civil protection authorities, a hydraulic model is necessary. Typical one- and two-dimensional hydraulic models are able to predict dynamically varying inundation extent, water depth and velocity for river and floodplain reaches up to 100 km in length. However, because of uncertainties over appropriate surface friction parameters, calibration of hydraulic models against observed data is a necessity. The value of different types of data is explored in constraining the predictions of a simple two-dimensional hydraulic model, LISFLOOD-FP. For the January 1995 flooding on the River Meuse, The Netherlands, a flow observation data set has been assembled for the 35-km reach between Borgharen and Maaseik, consisting of Synthetic Aperture Radar and air photo images of inundation extent, downstream stage and discharge hydrographs, two stage hydrographs internal to the model domain and 84 point observations of maximum free surface elevation. The data set thus contains examples of all the types of data that potentially can be used to calibrate flood inundation models. 500 realisations of the model have been conducted with different friction parameterisations and the performance of each realisation has been evaluated against each observed data set. Implementation of the Generalised Likelihood Uncertainty Estimation (GLUE) methodology is then used to determine the value of each data set in constraining the model predictions as well as the reduction in parameter uncertainty resulting from the updating of generalised likelihoods based on multiple data sources.