The use of spatial patterns of flood inundation (often obtained from remotely sensed imagery) to calibrate flood inundation models has been in widespread use over the last 15 years. Model calibration is most often achieved by employing one or even several performance measures derived from the well-known confusion matrix based on a binary classification of flooding. However, relatively early on, it has been recognized that the use of commonly reported performance measures for calibrating flood inundation models (such as the F measure) is hampered since the calibration procedure commonly utilizes only one possible solution of a wet/dry classification of a remote sensing image (most often acquired by a Synthetic Aperture Radar (SAR)) to calibrate or validate models and are biased towards either over- or under-prediction of flooding. Despite the call in several studies for an alternative statistic, to this date very few, if any, unbiased performance measure based on the confusion matrix has been proposed for flood model calibration/validation studies. In this paper we employ a robust statistical measure that operates in the receiver operating characteristics (ROC) space and allows automated model calibration with high identifiability of the best model parameter set but without the need of a classification of the SAR image. The ROC-based method for flood model calibration is demonstrated using two different flood event test cases with ood models of varying degree of complexity and boundary conditions with varying degree of accuracy. Verification of the calibration results and optional SAR classification is successfully performed with independent observations of the events. We believe that this proposed alternative approach to flood model calibration using spatial patterns of flood inundation should be employed instead of performance measures commonly used in conjunction with a binary flood map. This article is protected by copyright. All rights reserved.
Architecture, Civil Engineering, Surveying