nerc.ac.uk

Risk-based Probabilistic Fluvial Flood Forecasting for Integrated Catchment Models: Phase 3 Implementation Plan. Science Report – SR SC080030

Sene, Kevin; Weerts, Albrecht; Beven, Keith; Moore, Bob; Whitlow, Chris. 2010 Risk-based Probabilistic Fluvial Flood Forecasting for Integrated Catchment Models: Phase 3 Implementation Plan. Science Report – SR SC080030. Bristol, UK, Environment Agency, 35pp. (CEH Project Numbers: C03755 and C04217) (Unpublished)

Before downloading, please read NORA policies.
[thumbnail of Dissemination Status: for internal Environment Agency use only] Text (Dissemination Status: for internal Environment Agency use only)
SC080030_-_Phase_3_Implementation_Plan_-_revised_final.pdf - Submitted Version
Restricted to NORA staff only

Download (198kB) | Request a copy

Abstract/Summary

Robust forecasts are vital in providing a comprehensive Flood Warning Service to people and businesses at risk from flooding. For fluvial flood forecasting, rainfall-runoff, flow routing and hydraulic models are often combined into model cascades and are run automatically in the Environment Agency’s National Flood Forecasting System (NFFS). However, it is widely known that the accuracy of flood forecasts can be influenced by a number of factors, such as the accuracy of input data, and the model structure, parameters and state (initial conditions). Having a sound understanding of these modelling uncertainties is vital to assess and improve the flood forecasting service that the Environment Agency provides. This report presents the implementation plan developed during Phase 3 of the project SC080030 “Risk-based Probabilistic Fluvial Flood Forecasting for Integrated Catchment Models”. The main aim of the project is to develop and test practical probabilistic methods to quantify and, where possible, reduce uncertainties around fluvial flood forecasts from sources other than predicted rainfall. The project started in November 2008 and will complete in 2010. The Plan provides suggestions on the steps required to incorporate the probabilistic fluvial flood forecasting techniques developed during the project into existing forecasting systems (NFFS), and makes recommendations on which other improvements may be necessary to achieve this. Some factors which were considered in developing this Plan included: • The Environment Agency’s Implementing Probabilistic Forecasting Programme (IPFF) • Priorities of regional teams involved in parallel running/pilot tests • Current/planned NFFS developments • NFFS development/update cycle • Cost of implementation • Staff resources for implementation • Current/planned R&D projects • Current/planned computer hardware/software upgrades • Third-party dependencies/involvement (e.g. for hydrodynamic models) The Plan also includes an indicative prioritised programme of necessary works and staff resources to introduce probabilistic fluvial flood forecasting into NFFS on a risk basis. Suggestions are also made for how the most benefit from the project outputs and from the proposed techniques can be derived. The suggestions made could then inform the national approach to implementing probabilistic forecasting within the Environment Agency more generally.

Item Type: Publication - Report
Programmes: CEH Topics & Objectives 2009 - 2012 > Water > WA Topic 3 - Science for Water Management > WA - 3.1 - Develop next generation methods for river flow frequency estimation and forecasting
UKCEH and CEH Sections/Science Areas: Boorman (to September 2014)
Funders/Sponsors: Environment Agency, CEH Wallingford
Additional Information. Not used in RCUK Gateway to Research.: Dissemination Status: for internal Environment Agency use only
Additional Keywords: probabilistic, flood, forecasting, ensemble, catchment, rainfall-runoff, hydrodynamic
NORA Subject Terms: Meteorology and Climatology
Hydrology
Atmospheric Sciences
Related URLs:
Date made live: 11 Oct 2012 11:17 +0 (UTC)
URI: https://nora.nerc.ac.uk/id/eprint/13834

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...