Towards automatic calibration of 2-D flood propagation models

Abstract

Abstract. Hydraulic models for flood propagation description are an essential tool in many fields and are used, for example, for flood hazard and risk assessments, evaluation of flood control measures, etc. Nowadays there are many models of different complexity regarding the mathematical foundation and spatial dimensions available, and most of them are comparatively easy to operate due to sophisticated tools for model setup and control. However, the calibration of these models is still underdeveloped in contrast to other models like e.g. hydrological models or models used in ecosystem analysis. This has two primary reasons: first, lack of relevant data against which the models can be calibrated, because flood events are very rarely monitored due to the disturbances inflicted by them and the lack of appropriate measuring equipment in place. Second, 2-D models are computationally very demanding and therefore the use of available sophisticated automatic calibration procedures is restricted in many cases. This study takes a well documented flood event in August 2002 at the Mulde River in Germany as an example and investigates the most appropriate calibration strategy for a simplified 2-D hyperbolic finite element model. The model independent optimiser PEST, that enables automatic calibrations without changing model code, is used and the model is calibrated against over 380 surveyed maximum water levels. The application of the parallel version of the optimiser showed that (a) it is possible to use automatic calibration in combination of 2-D hydraulic model, and (b) equifinality of Correspondence to: P. Fabio (fabio@idra.unipa.it) model parameterisation can also be caused by a too large number of degrees of freedom in the calibration data in contrast to a too simple model setup. In order to improve model calibration and reduce equifinality, a method was developed to identify calibration data, resp. model setup with likely errors that obstruct model calibration.

Multi-objective automatic calibration of hydrodynamic models utilizing inundation maps and gauge data

“…The ranges are comparable to those published in hydraulic text books and other publications (Chow et al, 1988;Werner et al, 2005;Pappenberger et al, 2007b;Fabio et al, 2010). The ranges chosen enclose the values typically expected for the flow conditions, but are also large enough in order to enable compensation of model errors -most likely geometric errors -by the calibration.…”

“…Automatic calibration adjusts parameters automatically according to a specified search scheme optimising numerical measures of goodness of fit of the model results to the data. Automatic calibration gained increasing popularity in the past decades, because it alleviates the chief drawbacks of manual approaches which are subjective, tedious, much dependent on the expertise of modellers, and need huge amount of labour (Duan et al, 1993;Madsen, 2000;Fabio et al, 2010). A lot of research has been carried out for developing automated calibration routines or applying them to a large number of water-related applications (Duan et al, 1992;Solomatine et al, 1999;Skahill and Doherty, 2006;Bárdossy and Singh, 2008).…”

Assessing impacts of dike construction on the flood dynamics of the Mekong Delta

“…Among them, the river roughness coefficient was the most important and sensitive parameter. River roughness was represented in the model as Manning coefficients, which we initially estimated based on published values corresponding to particular types of rivers and canals (Chow, 1959;Fabio et al, 2010;Dung et al, 2011). First, referring to Chow (1959), we set the Manning coefficients as 0.020 (irrigation channel, straight, on hard-packed smooth sand), 0.025 (earth channel excavated in alluvial silt soil, with deposits of sand on the bottom and grass growth), and 0.033 (natural channel, somewhat irregular side slopes, very little variation in cross section).…”

A review of low‐cost space‐borne data for flood modelling: topography, flood extent and water level

“…For example, Fabio et al . () used 390 flood depths points to calibrate a comparatively simple 2D flood propagation model. The equifinality of the parameter sets showed that the model structure is not sufficient to properly describe the information content of the calibration data.…”