Risk-based Probabilistic Fluvial Flood Forecasting for Integrated Catchment Models:Phase 3 Guidelines. Science Report – SR SC080030
Sene, K.; Weerts, A.; Beven, K.; Moore, R.J.; Whitlow, C.; Beckers, P.; Minet, A.; Winsemius, H.; Verkade, J.; Young, P.; Leedal, D.; Smith, P.; Cole, S.; Robson, A.; Howard, P.; Craig, A.; Huband, M.; Breton, N.. 2010 Risk-based Probabilistic Fluvial Flood Forecasting for Integrated Catchment Models:Phase 3 Guidelines. Science Report – SR SC080030. Bristol, UK, Environment Agency, 171pp. (CEH Project Numbers: C03755 and C04217) (Unpublished)Before downloading, please read NORA policies.
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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 guidelines developed during Phase 3 of the R&D project ‘Probabilistic Fluvial Flood Forecasting’ (2008-2010). The main aim of the project was 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 aim of the guidelines is to provide up-to-date practical guidance on how to apply probabilistic fluvial flood forecasting techniques operationally. Suggestions are made on the most appropriate probabilistic flood forecasting approaches for different forecasting situations and how they should be applied. The target audience for the guidelines includes forecasting technical specialists and others involved in commissioning, maintaining and improving models. The guidelines are based on experience gained and techniques developed during Phase 2 of the project, which evaluated probabilistic techniques for the Upper Calder, Lower Eden, Ravensbourne and Upper Severn catchments in North East, North West, Thames and Midlands Regions. The locations for the case studies were selected following consultations with regional flood forecasting and warning staff during Phase 1 of the project. Following a general introduction to key concepts in probabilistic forecasting, and potential applications, the methods which are presented include a range of forward uncertainty propagation, data assimilation and forecast calibration techniques. A concise version of the uncertainty framework developed in Phase 2 of the project - tailored to the specific methods considered - is included as a guide to the selection of appropriate techniques. The forward uncertainty propagation techniques which are discussed include the following methods for propagating individual sources of uncertainty through integrated catchment models: • Rainfall inputs derived from raingauge weighting schemes • Model parameter uncertainty from MCRM, TCM and PDM rainfall-runoff models • Rating curve uncertainty These sources of uncertainty were identified as the priorities to consider during the consultations in Phase 1 of the project. Two data assimilation techniques are also described (adaptive gain, and the Data Based Mechanistic approach) which – unlike the current deterministic approaches used in the Environment Agency – as well as improving the forecast also provide an estimate of uncertainty. The guidelines also identify that forecast calibration (or conditioning) techniques have a key role to play in calibrating the probabilistic content of forecasts, based on long-runs of historical data (hindcasts). The methods which are considered are quantile regression and ARMA error prediction. Suggestions are also provided for the probabilistic performance measures which might be used, whilst noting that this topic is also being considered in other projects. Run-time issues are also discussed where they affect the methods considered, and the following options for reducing run-times are considered: computational improvements, and reconfiguration and emulators for hydrodynamic models.
|Item Type:||Publication - Report (UNSPECIFIED)|
|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|
|CEH Sections:||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
|Date made live:||11 Oct 2012 11:34|
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