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Global flood risk models were developed to identify risk hotspots in a world with increasing flood occurrence. Here we assess the ability and limitations of the current models and suggest what is needed moving forward. Table 1 | Links to models, tools, and programmes discussed in the text.

River width is a fundamental parameter of river hydrodynamic simulations, but no global‐scale river width database based on observed water bodies has yet been developed. Here we present a new algorithm that automatically calculates river width from satellite‐based water masks and flow direction maps. The Global Width Database for Large Rivers (GWD‐LR) is developed by applying the algorithm to the SRTM Water Body Database and the HydroSHEDS flow direction map. Both bank‐to‐bank river width and effective river width excluding islands are calculated for river channels between 60S and 60N. The effective river width of GWD‐LR is compared with existing river width databases for the Congo and Mississippi Rivers. The effective river width of the GWD‐LR is slightly narrower compared to the existing databases, but the relative difference is within ±20% for most river channels. As the river width of the GWD‐LR is calculated along the river channels of the HydroSHEDS flow direction map, it is relatively straightforward to apply the GWD‐LR to global and continental‐scale river modeling.

ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Abstract 17This paper describes the development of a Global 3 arc-second Water Body Map (G3WBM), 18 using an automated algorithm to process multi-temporal Landsat images from the Global Land 19 Survey (GLS) database. We used 33,890 scenes from 4 GLS epochs in order to delineate a 20 seamless water body map, without cloud and ice/snow gaps. Permanent water bodies were 21 distinguished from temporal water-covered areas by calculating the frequency of water body 22 existence from overlapping, multi-temporal, Landsat scenes. By analyzing the frequency of 23 water body existence at 3 arc-second resolution, the G3WBM separates river channels and 24 floodplains more clearly than previous studies. This suggests that the use of multi-temporal 25 images is as important as analysis at a higher resolution for global water body mapping. The 26 global totals of delineated permanent water body area and temporal water-covered area are 3.25 27 and 0.49 million km 2 respectively, which highlights the importance of river-floodplain 28 separation using multi-temporal images. The accuracy of the water body classification was 29 2 validated in Hokkaido (Japan) and in the contiguous United States using an existing water body 30 databases. There was almost no commission error, and about 70% of lakes >1 km 2 shows 31 relative water area error <25%. Though smaller water bodies (<1 km 2 ) were underestimated 32 mainly due to omission of shoreline pixels, the overall accuracy of the G3WBM should be 33 adequate for larger scale research in hydrology, biogeochemistry, and climate systems and 34 importantly includes a quantification of the temporal nature of global water bodies. 35 Keywords 36Landsat GLS, water body mapping, global analysis, river, floodplain 37

Quantifying flood hazard is an essential component of resilience planning, emergency response, and mitigation, including insurance. Traditionally undertaken at catchment and national scales, recently, efforts have intensified to estimate flood risk globally to better allow consistent and equitable decision making. Global flood hazard models are now a practical reality, thanks to improvements in numerical algorithms, global datasets, computing power, and coupled modelling frameworks. Outputs of these models are vital for consistent quantification of global flood risk and in projecting the impacts of climate change. However, the urgency of these tasks means that outputs are being used as soon as they are made available and before such methods have been adequately tested. To address this, we compare multi-probability flood hazard maps for Africa from six global models and show wide variation in their flood hazard, economic loss and exposed population estimates, which has serious implications for model credibility. While there is around 30%-40% agreement in flood extent, our results show that even at continental scales, there are significant differences in hazard magnitude and spatial pattern between models, notably in deltas, arid/semi-arid zones and wetlands. This study is an important step towards a better understanding of modelling global flood hazard, which is urgently required for both current risk and climate change projections.

[1] Hydrodynamic modeling of large remote forested floodplains, such as the Amazon, is hindered by the vegetation signal contained within Digital Elevation Models (DEMs) such as the Shuttle Radar Topography Mission (SRTM). Not removing the vegetation signal causes DEMs to be overelevated preventing the correct simulation of overbank inundation. Previous efforts to remove this vegetation signal have either not accounted for its spatial variability or relied upon single assumed error values. As a possible solution, a systematic approach to removing the vegetation signal which accounts for spatial variability using recently published estimates of global vegetation heights is proposed. The proposed approach is applied to a well-studied reach of the Amazon floodplain where previous hydrodynamic model applications were affected by the SRTM vegetation signal. Greatest improvements to hydrodynamic model accuracy were obtained by subtracting 50-60% of the vegetation height from the SRTM. The vegetation signal removal procedure improved the RMSE (Root-Mean-Square Error) accuracy of the hydrodynamic model than when using the original SRTM in three ways: (1) seasonal floodplain water elevation predictions against TOPEX/Poseidon observations improved from 6.61 to 1.84 m; (2) high water inundation extent prediction accuracy improved from 0.52 to 0.07 against a JERS (Japanese Earth Resources Satellite) observation; (3) low water inundation extent accuracy against a JERS observation improved from 0.22 to 0.12. The simple data requirements of this vegetation removal method enable it to be applied to any remote floodplain for which hydrodynamic model accuracy is hindered by vegetation present in the DEM.

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[1] Floodplain channels are important components of river-floodplain systems and are known to play a key role in hydrodynamic exchange and sediment transport. The Amazon floodplain exhibits complex networks of these channels, and despite their potential importance to this globally important wetland system, these floodplain channels are relatively unstudied. The research presented here is the first systematic and detailed study of the network and morphologic characteristics of a large number of these channels in the middle reach of the central Amazon River using analysis of data derived from Landsat Enhanced Thematic Mapper Plus (ETMþ) mosaic and field survey. Our findings show that the channels are ubiquitous, their width varies widely, and some of their characteristics can be fitted using power laws, potentially much like the self-similar or fractal-like behavior hypothesized for other types of fluvial networks. In all, 96% of the floodplain channels are not wide enough to be represented well, or at all, in the $90 m Shuttle Radar Topography Mission data. Channel depths are tied closely to the local amplitude of the passing main river flood wave (p value of 0.75), except where there are local runoff inputs, which results in substantially deeper channels which provide preferential flow paths across the floodplain. Channel networks imply that areas of the floodplain function for large parts of the flood cycle as separate hydrogeomorphic land units, here termed floodplain hydrological units (FHUs). These hypothesized FHUs also have distinct spatial and pattern characteristics, and it is suggested here that their differences could provide the beginnings of a framework for understanding the detailed hydrodynamics of the floodplain. In particular, different types of FHUs have differences in flood water source, which will have important implications for biogeochemical studies of the wetlands.