Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data

Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model

“…This problem of being unable to properly constrain the parameter space is closely linked to the equifinality problem, i.e. different 60 parameter sets yield similar model results with respect to the objective function used in the calibration (Beven and Binley, 1992;Aronica et al, 1998Aronica et al, , 2002Horritt and Bates, 2002;Pappenberger et al, 2005;Werner et al, 2005). The parameter sets that lead to the same optimal solution for the calibration problem are often defined as 'behavioural' parameter sets (Beven, 1996).…”

“…Among alternatives to evaluate the model's performance distributedly, are the use of post-flood field survey data (Aronica et al, 1998;Hunter et al, 2005;Neal et al, 2009), aerial photos or airborne and spaceborne Synthetic Aperture Radar (SAR) data (Bates and De Roo, 2000;Aronica et al, 2002;Horritt et al, 20 2007; Di Baldassarre et al, 2009a). Especially, radar, with its day, night and cloud penetrating capacity, is a promising technology for supporting flood inundation modelling (Bates, 2004;Montanari et al, 2009;Schumann et al, 2009b;Tarpanelli et al, 2012).…”

“…Therefore, we restrict the calibration to only the roughness parameters (e.g. as in Romanowicz et al 80 (1996); Aronica et al (1998);Horritt and Bates (2001); Hostache et al (2009);Di Baldassarre et al (2009a); Schumann et al (2014)). We focus on the River Dee, which has been used several times to test different hypotheses and techniques 4 related to flood inundation modelling (Di Baldassarre et al, 2009a;Stephens et al, 2012;Schumann et al, 2014).…”

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

“…Data fusion methodology used in CITI-SENSE Project to integrate dynamic air quality observations (adapted from Schneider et al, 2015) Beside DA applications, intermittent (or short duration) and distributed data can be used for model calibration as shown by Sheffield et al (2006) and Seibert and Beven (2009). Aronica et al (1998) proposed a fuzzy-rule-based calibration to compare model predictions and highly uncertain information about the flood arising from several different different types of observations. Seibert and McDonnell (2002) proposed an approach to calibrate hydrological models using both hard and soft data (e.g.…”

“…Although some efforts to validate model results against such observations have been made, these are mainly done in a post-event analysis (Aronica et al, 1998;Seibert and McDonnell 2002;Vaché et al 2004;Sheffield et al 2006;Seibert and Beven 2009). The added value of information coming from citizens, therefore, is not typically integrated into hydrological and/or hydraulic models Nowadays, model updating occurs only in the form of data assimilation using measurements of streamflow, soil moisture, etc.…”