Susceptibility of catchments to INTense RAinfall and flooding (Project SINATRA)

Lead Research Organisation: University of Bristol
Department Name: Geographical Sciences

Abstract

Project SINATRA responds to the NERC call for research on flooding from intense rainfall (FFIR) with a programme of focused research designed to advance general scientific understanding of the processes determining the probability, incidence, and impacts of FFIR.

Such extreme rainfall events may only last for a few hours at most, but can generate terrifying and destructive floods. Their impact can be affected by a wide range factors (or processes) such as the location and intensity of the rainfall, the shape and steepness of the catchment it falls on, how much sediment is moved by the water and the vulnerability of the communities in the flood's path. Furthermore, FFIR are by their nature rapid, making it very difficult for researchers to 'capture' measurements during events. The complexity, speed and lack of field measurements on FFIR make it difficult to create computer models to predict flooding and often we are uncertain as to their accuracy.

To address these issues, NERC launched the FFIR research programme. It aims to reduce the risks from surface water and flash floods by improving our identification and prediction of the meteorological (weather), hydrological (flooding) and hydro-morphological (sediment and debris moved by floods) processes that lead to FFIR. A major requirement of the programme is identifying how particular catchments may be vulnerable to FFIR, due to factors such as catchment area, shape, geology and soil type as well as land-use. Additionally, the catchments most susceptible to FFIR are often small and ungauged.

Project SINATRA will address these issues in three stages: Firstly increasing our understanding of what factors cause FFIR and gathering new, high resolution measurements of FFIR; Secondly using this new understanding and data to improve models of FFIR so we can predict where they may happen - nationwide and; Third to use these new findings and predictions to provide the Environment Agency and over professionals with information and software they can use to manage FFIR, reducing their damage and impact to communities.
In more detail, we will:
1. Enhance scientific understanding of the processes controlling FFIR, by-
(a) assembling an archive of past FFIR events in Britain and their impacts, as a prerequisite for improving our ability to predict future occurrences of FFIR.
(b) making real time observations of flooding during flood events as well as post-event surveys and historical event reconstruction, using fieldwork and crowd-sourcing methods.
(c) characterising the physical drivers for UK summer flooding events by identifying the large-scale atmospheric conditions associated with FFIR events, and linking them to catchment type.
2. Develop improved computer modelling capability to predict FFIR processes, by-
(a) employing an integrated catchment/urban scale modelling approach to FFIR at high spatial and temporal scales, modelling rapid catchment response to flash floods and their impacts in urban areas.
(b) scaling up to larger catchments by improving the representation of fast riverine and surface water flooding and hydromorphic change (including debris flow) in regional scale models of FFIR.
(c) improving the representation of FFIR in the JULES land surface model by integrating river routing and fast runoff processes, and performing assimilation of soil moisture and river discharge into the model run.
3. Translate these improvements in science into practical tools to inform the public more effectively, by-
(a) developing tools to enable prediction of future FFIR impacts to support the Flood Forecasting Centre in issuing new 'impacts-based' warnings about their occurrence.
(b) developing a FFIR analysis tool to assess risks associated with rare events in complex situations involving incomplete knowledge, analogous to those developed for safety assessment in radioactive waste management.

In so doing SINATRA will achieve NERC's science goals for the FFIR programme.

Planned Impact

SINATRA will deliver a number of important benefits for our immediate UK project partners and for the wider public, who will ultimately be served by more effective flood forecasting and management systems, both in the UK and beyond.

SINATRA will help the Met Office, the Environment Agency, and their joint Flood Forecasting Centre (FFC) meet the demands of the Pitt Review (2008: vii) for a "a step change in the quality of flood warnings" and in their capacity to forecast groundwater, surface water and other kinds of flooding from intense rainfall (FFIR).

Beyond the UK, SINATRA's findings will also be of benefit to forecasters dealing with similar challenges elsewhere, including the European Centre for Medium Range Weather Forecasts (ECMWF), Swedish Meteorological and Hydrological Institute (SMHI) and Dutch Rijkswaterstaat, the executive water management organisation of the Ministry of Infrastructure and the Environment, who have all provided letters of support outlining their interest in the project.

By improving the basis for assessing impacts, SINATRA will also make important contributions to fulfilling the strategic aims of the Cabinet Office's National Hazards Partnership and to meeting the demands made by the expressed by the Met Office Public Weather Service Customer Group, on behalf of the civil contingencies community, for more proportionate and meaningful warnings

At the local and regional scale, SINATRA will also improve the evidence-base on catchment susceptibility factors needed by Local Authorities to fulfil their new duties under the 2010 Flood and Water Management Act to be the lead agencies responsible for the management of flood risk from surface runoff, groundwater, and small (so-called "ordinary") watercourses. The database of FFIR events and impacts, as well as the analysis of extreme value statistics and of catchment susceptibility factors, will also help critical infrastructure providers, the insurance industry and others across the private sector to appreciate their exposure to FFIR.

Publications

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Zischg A (2018) Effects of variability in probable maximum precipitation patterns on flood losses in Hydrology and Earth System Sciences

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Van Eerdenbrugh K (2017) Consistency assessment of rating curve data in various locations using Bidirectional Reach (BReach) in Hydrology and Earth System Sciences

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García-Pintado J (2015) Satellite-supported flood forecasting in river networks: A real case study in Journal of Hydrology

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Westerberg I (2016) Uncertainty in hydrological signatures for gauged and ungauged catchments in Water Resources Research

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Sampson CC (2015) A high-resolution global flood hazard model. in Water resources research

 
Title Gridded estimates of hourly areal rainfall for Great Britain (1990-2014) [CEH-GEAR1hr] 
Description The dataset contains 1km gridded estimates of hourly rainfall for Great-Britain for the period 1990-2014. The estimates are derived by applying the nearest neighbour interpolation method to a national database of hourly raingauge observations collated by Newcastle University and the Centre for Ecology & Hydrology (CEH). These interpolated hourly estimates were then used to temporally disaggregate the CEH-GEAR daily rainfall dataset. The estimated rainfall on a given hour refers to the rainfall amount accumulated in the previous hour. The dataset also contains data indicating the distance between the grid point and the closest recording raingauge used in its interpolation. When this distance is greater than 50km, or there is zero rainfall recorded in the closest gauge, the daily value is disaggregated using a design storm. The dataset therefore also contains a flag indicating if the design storm was used. These data are provided as an indicator of the quality of the estimates. 
Type Of Material Database/Collection of data 
Year Produced 2019 
Provided To Others? Yes  
Impact Only recently added database so no core outcomes yet 
URL https://catalogue.ceh.ac.uk/documents/d4ddc781-25f3-423a-bba0-747cc82dc6fa