Abstract. The Centre for Ecology & Hydrology -Gridded Estimates of Areal Rainfall (CEH-GEAR) data set was developed to provide reliable 1 km gridded estimates of daily and monthly rainfall for Great Britain (GB) and Northern Ireland (NI) (together with approximately 3500 km 2 of catchment in the Republic of Ireland) from 1890 onwards. The data set was primarily required to support hydrological modelling.The rainfall estimates are derived from the Met Office collated historical weather observations for the UK which include a national database of rain gauge observations. The natural neighbour interpolation methodology, including a normalisation step based on average annual rainfall (AAR), was used to generate the daily and monthly rainfall grids. To derive the monthly estimates, rainfall totals from monthly and daily (when complete month available) rain gauges were used in order to obtain maximum information from the rain gauge network. The daily grids were adjusted so that the monthly grids are fully consistent with the daily grids. The CEH-GEAR data set was developed according to the guidance provided by the British Standards Institution.The CEH-GEAR data set contains 1 km grids of daily and monthly rainfall estimates for GB and NI for the period 1890-2012. For each day and month, CEH-GEAR includes a secondary grid of distance to the nearest operational rain gauge. This may be used as an indicator of the quality of the estimates. When this distance is greater than 100 km, the estimates are not calculated due to high uncertainty.CEH-GEAR is available from doi:10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e and is free of charge for commercial and non-commercial use subject to licensing terms and conditions.
ABSTRACT:On the 8 January 2005 the city of Carlisle in north-west England was severely flooded following 2 days of almost continuous rain over the nearby hills. Orographic enhancement of the rain through the seeder-feeder mechanism led to the very high rainfall totals. This paper shows the impact of running the Met Office Unified Model (UM) with a grid spacing of 4 and 1 km compared to the 12 km available at the time of the event. These forecasts, and forecasts from the Nimrod nowcasting system, were fed into the Probability Distributed Model (PDM) to predict river flow at the outlets of two catchments important for flood warning. The results show the benefit of increased resolution in the UM, the benefit of coupling the high-resolution rainfall forecasts to the PDM and the improvement in timeliness of flood warning that might have been possible.
How might climate change affect river flows across the Thames Basin?: an area-wide analysis using the UKCP09 Regional Climate Model ensemble. Hydrology, 442-443. 89-104. 10.1016Hydrology, 442-443. 89-104. 10. /j.jhydrol.2012 Contact CEH NORA team at email@example.com
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14The Thames Basin drains an area of over 10,000km 2 through London to the North Sea. It 15 encompasses both rural and heavily urbanised areas overlying a spatially-varied and complex 16 geology. Historically, the lower Thames has proved resilient to climate variability, and careful 17 river management in recent years has helped protect the region from flooding. However, 18 recent climate projections for the region indicate that over the next century winter rainfall 19 might increase by 10-15%, potentially leading to higher flows than the Thames can 20 accommodate. This study uses a distributed hydrological model, the Grid-to-Grid (G2G), to 21 assess future changes in peak river flows for a range of catchments across the Thames Basin. 22
Existing surface water flood forecasting methods in Scotland are based on indicative depth‐duration rainfall thresholds with limited understanding of the likelihood of inundation or associated impacts. Innovative risk‐based solutions are urgently needed to advance surface water forecasting capabilities for improved flood resilience in urban centres. A new model‐based solution was developed for Glasgow, linking 24‐h ensemble rainfall predictions from the Met Office Global and Regional Ensemble Prediction System for the UK (MOGREPS‐UK) with static flood risk maps through the Grid‐to‐Grid hydrological model. This new forecasting capability was used operationally by the Scottish Flood Forecasting Service during the 2014 Commonwealth Games to provide bespoke surface water flooding guidance to responders. The operational trial demonstrated the benefits of being able to provide targeted information on real‐time surface water flood risk. It also identified the high staff resource requirement to support the service due to the greater uncertainty in surface water flood forecasting compared to established fluvial and coastal methods.
Abstract. Major UK floods over the last decade have motivated significant technological and scientific advances in operational flood forecasting and warning. New joint forecasting centres between the national hydrological and meteorological operating agencies have been formed that issue a daily, national Flood Guidance Statement (FGS) to the emergency response community. The FGS is based on a Flood Risk Matrix approach that is a function of potential impact severity and likelihood. It has driven an increased demand for robust, accurate and timely forecast and alert information on fluvial and surface water flooding along with impact assessments. The Grid-to-Grid (G2G) distributed hydrological model has been employed across Britain at a 1km resolution to support the FGS. Novel methods for linking dynamic gridded estimates of river flow and surface runoff with more detailed offline flood risk maps have been developed to obtain real-time probabilistic forecasts of potential impacts, leading to operational trials. Examples of the national-scale G2G application are provided along with case studies of forecast flood impact from (i) an operational Surface Water Flooding (SWF) trial during the Glasgow 2014 Commonwealth Games, (ii) SWF developments under the Natural Hazards Partnership over England & Wales, and (iii) fluvial applications in Scotland.
Surface water flooding (SWF) is a recurrent hazard that affects lives and livelihoods.Climate change is projected to change the frequency of extreme rainfall events that can lead to SWF. Increasingly, data from Regional Climate Models (RCMs) are being used to investigate the potential water-related impacts of climate change; such assessments often focus on broad-scale fluvial flooding and the use of coarse resolution (>12 km) RCMs. However, high-resolution (<4 km) convection-permitting RCMs are now becoming available that allow impact assessments of more localised SWF to be made.At the same time, there has been an increasing demand for more robust and timely real-time forecast and alert information on SWF. In the UK, a real-time SWF Hazard Impact Model framework has been developed. The system uses 1-km gridded surface runoff estimates from a hydrological model to simulate the SWF hazard. These are linked to detailed inundation model outputs through an Impact Library to assess impacts on property, people, transport, and infrastructure for four severity levels.Here, a set of high-resolution (1.5 km and 12 km) RCM data has been used as input to a grid-based hydrological model over southern Britain to simulate Current (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) and Future (~2100s; RCP8.5) surface runoff. Counts of thresholdexceedance for surface runoff and precipitation (at 1-, 3-and 6-hr durations) are analysed. Results show that the percentage increases in surface runoff extremes, are less than those of precipitation extremes. The higher-resolution RCM simulates the largest percentage increases, which occur in winter, and the winter exceedance counts are greater than summer exceedance counts. For property impacts, the largest percentage increases are also in winter; however, it is the 12-km RCM output that leads to the largest percentage increase in impacts. The added-value of highresolution climate model data for hydrological modelling is from capturing the more intense convective storms in surface runoff estimates.
K E Y W O R D Sclimate change, hazard, impact, modelling, pluvial flooding, surface water flooding
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