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Integrating remote sensing data with flood inundation models: how far have we got?

Copyright © 2012 John Wiley & Sons, Ltd. In an article in Hydrological Processes in 2004 (Bates, 2004), I argued that the use of remote sensing data had allowed a significant breakthrough to be made in flood inundation modelling. Over the preceding 5 years, flood inundation research had moved rapidly from being a ‘data-poor’ to a ‘data-rich’ science (see also Di Baldassarre and Uhlenbrook, 2011), with attendant possibilities for model development and process insight. Until the late 1990s, the only data available to build, parameterize, calibrate and validate hydraulic models were from limited ground topographic surveys and sparse ground gauging stations with spacings of between 10 and 60 km. Occasionally, air photos of flooding were available, but these were not used to test model performance in a systematic way because the available terrain data were insufficiently detailed to provide confidence in distributed model predictions. The errors in the then-available data and their limited information content meant that it was very difficult to discriminate between different model physics and parameters, such that many different models could fit the available validation data equally well yet lead to different future predictions or process inferences. This equifinal behaviour also retarded model development and testing, and in particular prevented the more widespread adoption of two-dimensional (2D) hydraulic models in two main ways. First, the ground survey terrain data that were available were typically captured as a series of cross sections perpendicular to the channel and floodplain that were more easily integrated with one-dimensional (1D) models. Second, the use of widely spaced point-gauge data for calibration and validation meant that the only aspect of model performance that could be tested effectively was the ability of a model to route a wave in 1D along a river network. Under the circumstances, 2D models added considerable additional complexity for no discernible net benefit in prediction (see Bates et al., 1998 for an example of an overspecified 2D model tested in terms of its flow routing performance only). The breakthrough came with the advent of remote sensing techniques for wide-area topographic mapping, principally using airborne laser altimetry (LiDAR) (Gomes-Pereira and Wicherson, 1999; Marks and Bates, 2000; French, 2003; Bates et al., 2003). Such data were then captured at approximately 2–5m spatial resolution at survey rates of up to 50 km h 1 and had a vertical accuracy of approximately 10–15 cm Root Mean Square Error (RMSE) over flat, hard surfaces. This contrasted strongly with ground survey data, which, although being of millimetric accuracy, were slow to capture and only available as cross sections every few hundred metres along a river channel (at best). LiDAR data were transformative, and raised the expectation that models built using them should be able to predict inundation extent accurately. Moreover, because LiDAR data also contained information on vegetation height, it was also possible to start to use this information to constrain floodplain friction parameters where vegetation drag is a major contributor to total frictional losses (Cobby et al., 2003; Mason et al., 2003, Straatsma and Baptist, 2008). Accordingly, modellers soon began to look for sources of remotely sensed