A toolbox to quickly prepare flood inundation models for LISFLOOD-FP simulations

Hydrodynamic floodplain inundation models have been popular for many years and used extensively in engineering applications. Continental scale flood studies are now achievable using such models due to the development of terrain elevation, hydrography and river width datasets with global coverage. However, deploying flood models at any scale is time-consuming since input data needs to be processed from different sources. Here we present LFPtools, which is an open-source Python package which encompasses most commonly used methods to prepare input data for large scale flood inundation studies using the LISFLOOD-FP hydrodynamic model. LFPtools performance was verified over the Severn basin in the UK where a 1 km flood inundation model was built within 1.45 min. Outputs of the test case were compared with the official flood extent footprint of a real event and satisfactory model performance was obtained: Hit rate = 0.79, False alarm ratio = 0.24 and Critical success index = 0.63

Creating the 2011 area classification for output areas (2011 OAC)

This paper presents the methodology that has been used to create the 2011 Area Classification for Output Areas (2011 OAC). This extends a lineage of widely used public domain census only geodemographic classifications in the UK. It provides an update to the successful 2001 OAC methodology, and summarizes the social and physical structure of neighbourhoods using data from the 2011 UK Census. We also present the results of a user engagement exercise that underpinned the creation of an updated methodology for the 2011 OAC. The 2011 OAC comprises 8 Supergroups, 26 Groups and 76 Subgroups. Finally, we present an example of the results of the classification in Southampton

Perspectives on open access high resolution digital elevation models to produce global flood hazard layers

Global flood hazard models have recently become a reality thanks to the release of open access global digital elevation models, the development of simplified and highly efficient flow algorithms, and the steady increase in computational power. In this commentary we argue that although the availability of open access global terrain data has been critical in enabling the development of such models, the relatively poor resolution and precision of these data now limit significantly our ability to estimate flood inundation and risk for the majority of the planet’s surface. The difficulty of deriving an accurate ‘bare-earth’ terrain model due to the interaction of vegetation and urban structures with the satellite-based remote sensors means that global terrain data are often poorest in the areas where people, property (and thus vulnerability) are most concentrated. Furthermore, the current generation of open access global terrain models are over a decade old and many large floodplains, particularly those in developing countries, have undergone significant change in this time. There is therefore a pressing need for a new generation of high resolution and high vertical precision open access global digital elevation models to allow significantly improved global flood hazard models to be developed

A climate-conditioned catastrophe risk model for UK flooding

We present a transparent and validated climate-conditioned catastrophe flood model for the UK, that simulates pluvial, fluvial and coastal flood risks at 1 arcsec spatial resolution (∼ 20–25 m). Hazard layers for 10 different return periods are produced over the whole UK for historic, 2020, 2030, 2050 and 2070 conditions using the UK Climate Projections 2018 (UKCP18) climate simulations. From these, monetary losses are computed for five specific global warming levels above pre-industrial values (0.6, 1.1, 1.8, 2.5 and 3.3 ∘C). The analysis contains a greater level of detail and nuance compared to previous work, and represents our current best understanding of the UK's changing flood risk landscape. Validation against historical national return period flood maps yielded critical success index values of 0.65 and 0.76 for England and Wales, respectively, and maximum water levels for the Carlisle 2005 flood were replicated to a root mean square error (RMSE) of 0.41 m without calibration. This level of skill is similar to local modelling with site-specific data. Expected annual damage in 2020 was GBP 730 million, which compares favourably to the observed value of GBP 714 million reported by the Association of British Insurers. Previous UK flood loss estimates based on government data are ∼ 3× higher, and lie well outside our modelled loss distribution, which is plausibly centred on the observations. We estimate that UK 1 % annual probability flood losses were ∼ 6 % greater for the average climate conditions of 2020 (∼ 1.1 ∘C of warming) compared to those of 1990 (∼ 0.6 ∘C of warming), and this increase can be kept to around ∼ 8 % if all countries' COP26 2030 carbon emission reduction pledges and “net zero” commitments are implemented in full. Implementing only the COP26 pledges increases UK 1 % annual probability flood losses by 23 % above average 1990 values, and potentially 37 % in a “worst case” scenario where carbon reduction targets are missed and climate sensitivity is high.</p

Steric Shielding of Surface Epitopes and Impaired Immune Recognition Induced by the Ebola Virus Glycoprotein

Many viruses alter expression of proteins on the surface of infected cells including molecules important for immune recognition, such as the major histocompatibility complex (MHC) class I and II molecules. Virus-induced downregulation of surface proteins has been observed to occur by a variety of mechanisms including impaired transcription, blocks to synthesis, and increased turnover. Viral infection or transient expression of the Ebola virus (EBOV) glycoprotein (GP) was previously shown to result in loss of staining of various host cell surface proteins including MHC1 and β1 integrin; however, the mechanism responsible for this effect has not been delineated. In the present study we demonstrate that EBOV GP does not decrease surface levels of β1 integrin or MHC1, but rather impedes recognition by steric occlusion of these proteins on the cell surface. Furthermore, steric occlusion also occurs for epitopes on the EBOV glycoprotein itself. The occluded epitopes in host proteins and EBOV GP can be revealed by removal of the surface subunit of GP or by removal of surface N- and O- linked glycans, resulting in increased surface staining by flow cytometry. Importantly, expression of EBOV GP impairs CD8 T-cell recognition of MHC1 on antigen presenting cells. Glycan-mediated steric shielding of host cell surface proteins by EBOV GP represents a novel mechanism for a virus to affect host cell function, thereby escaping immune detection

A high-resolution global flood hazard model

Floods are a natural hazard that affect communities worldwide, but to date the vast majority of flood hazard research and mapping has been undertaken by wealthy developed nations. As populations and economies have grown across the developing world, so too has demand from governments, businesses, and NGOs for modeled flood hazard data in these data-scarce regions. We identify six key challenges faced when developing a flood hazard model that can be applied globally and present a framework methodology that leverages recent cross-disciplinary advances to tackle each challenge. The model produces return period flood hazard maps at ∼90 m resolution for the whole terrestrial land surface between 56°S and 60°N, and results are validated against high-resolution government flood hazard data sets from the UK and Canada. The global model is shown to capture between two thirds and three quarters of the area determined to be at risk in the benchmark data without generating excessive false positive predictions. When aggregated to ∼1 km, mean absolute error in flooded fraction falls to ∼5%. The full complexity global model contains an automatically parameterized subgrid channel network, and comparison to both a simplified 2-D only variant and an independently developed pan-European model shows the explicit inclusion of channels to be a critical contributor to improved model performance. While careful processing of existing global terrain data sets enables reasonable model performance in urban areas, adoption of forthcoming next-generation global terrain data sets will offer the best prospect for a step-change improvement in model performance.</p