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This paper demonstrates the potential for crowdsourced rainfall data to infill gaps in the official rain gauge network and to provide new datasets for use in research. We use data from the Met Office Weather Observation Website (WOW) over 10 years (2011-2020) to generate two open-source datasets for Britain; multi-parameter raw data in an easy-to-use format; and an hourly rainfall dataset. We have compiled and prepared the data and detail here station selection, rain depth calculation, and data resampling to hourly intervals to create a consistent dataset for further processing (including statistical quality control) and application. Mapping the new rainfall dataset establishes that WOW observations fill spatial gaps in the official ground-based rain gauge network over Britain, particularly in urban areas. This could be particularly useful for post-event analysis of rainfall that results in pluvial flash flooding. Here, we focus on Britain but due to agreements with meteorological services in Belgium, the Netherlands, Australia, New Zealand, Sweden, and the Republic of Ireland, plus many citizen scientists globally opting to share data via WOW, there is potential for the development of similar datasets using these methods around the world. [ABSTRACT FROM AUTHOR]

Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis and in areas of data scarcity. However, the consequence of this for risk estimation has not been fully explored. We investigate the implications of four reanalysis products (ERA-5, CFSR, MERRA-2 and JRA-55) on simulations of historic flood events in five basins in England. These results are compared to a benchmark national gauge-based product (CEH-GEAR1hr). The benchmark demonstrated better accuracy than reanalysis products when compared with observations of water depth and flood extent. All reanalysis products predicted fewer buildings would be inundated by the events than the national dataset. JRA-55 was the worst by a significant margin, underestimating by 40 % compared with 14 %–18 % for the other reanalysis products. CFSR estimated building inundation the most accurately, while ERA-5 demonstrated the lowest error in terms of river stage (29.4 %) and floodplain depth (28.6 %). Accuracy varied geographically, and no product performed best across all basins. Global reanalysis products provide a useful resource for flood modelling where no other data are available, but they should be used with caution due to the underestimation of impacts shown here. Until a more systematic international strategy for the collection of rainfall and flood impact data ensures more complete global coverage for validation, multiple reanalysis products should be used concurrently to capture the range of uncertainties. [ABSTRACT FROM AUTHOR]

Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis, and in areas of data scarcity. However, the consequence of this for risk estimation has not been fully explored. We investigate the impact of using four reanalysis products (ERA-5, CFSR, MERRA-2 and JRA-55) on simulations of historic flood events in Northern England. These results are compared to a benchmark national gauge-based product (CEH-GEAR1hr). All reanalysis products predicted fewer buildings would be inundated by the events than the national dataset. JRA-55 was the worst by a significant margin, underestimating by 40% compared with 14-18% for the other reanalysis products. CFSR estimated building inundation the most accurately, while ERA-5 demonstrated the lowest error in terms of river stage (29.4%) and floodplain depth (28.6%). Accuracy varied geographically and no product performed the best across all basins. Global reanalysis products provide a useful resource for flood modelling where no other data is available, but they should be used with caution. Until a more systematic international strategy for the collection of rainfall data ensures more complete global coverage of validation data, multiple reanalysis products should be used concurrently to capture the range of uncertainties. [ABSTRACT FROM AUTHOR]

As urban populations grow, it is increasingly important to accurately characterize flood risk in cities and built up areas. Global digital elevation models (GDEMs) have recently enabled flood risk analysis at broad scale and worldwide, but their accuracy and its impact on modeled flood risk in cities has not been fully investigated. We compare flood extents, hydrographs, depths, and impacts between hydrodynamic simulations, using five spaceborne GDEM products and an airborne LIDAR product. Benchmark observations of a historical flood event in Carlisle (UK) were used to assess the accuracy of each simulation. GDEM simulations are shown to perform significantly less accurately than the airborne LIDAR‐based simulations. No DEM outperforms the others across all metrics; the MERIT DEM is the best predictor of flood extent, but TanDEM‐X performs best for discharge. However, the impacts of flooding from GDEM simulations are consistently overestimated, 2 to 3 times higher than those from LIDAR simulations. Until a high resolution, accurate, global DEM is available, multiple products should be used concurrently to enable the full uncertainty range to be quantified and communicated, to ensure flood risk management decisions are not misinformed. Key Points: Flood models using five global DEMs are worse at predicting flood extents, hydrographs, and floodplain depths than a model using LIDARGlobal DEM flood models consistently overestimate the impacts of flooding in a city by 2–3 times compared to a model using LIDARNo single global DEM performs best across all validation metrics, so multiple DEMs should be used to capture uncertainties in flood risk [ABSTRACT FROM AUTHOR]

Extreme short-duration rainfall can cause devastating flooding that puts lives, infrastructure, and natural ecosystems at risk. It is therefore essential to understand how this type of extreme rainfall will change in a warmer world. A significant barrier to answering this question is the lack of sub-daily rainfall data available at the global scale. To this end, a global sub-daily rainfall dataset based on gauged observations has been collated. The dataset is highly variable in its spatial coverage, record length, completeness and, in its raw form, quality. This presents significant difficulties for many types of analyses. The dataset currently comprises 23 687 gauges with an average record length of 13 years. Apart from a few exceptions, the earliest records begin in the 1950s. The Global Sub-Daily Rainfall Dataset (GSDR) has wide applications, including improving our understanding of the nature and drivers of sub-daily rainfall extremes, improving and validating of high-resolution climate models, and developing a high-resolution gridded sub-daily rainfall dataset of indices. [ABSTRACT FROM AUTHOR]