Testing probabilistic adaptive real‐time flood forecasting models

Operational flood forecasting has become a complex and multifaceted task, increasingly being treated in probabilistic ways to allow for the inherent uncertainties in the forecasting process. This paper reviews recent applications of data-based mechanistic (DBM) models within the operational UK National Flood Forecasting System. The position of DBM models in the forecasting chain is considered along with their offline calibration and validation. The online adaptive implementation with assimilation of water level information as used for forecasting is outlined. Two example applications based upon UK locations where severe flooding has occurred, the River Eden at Carlisle and River Severn at Shrewsbury, are presented.

[1]  Lennart Ljung,et al.  On positive real transfer functions and the convergence of some recursive schemes , 1977 .

[2]  P. Young.  Some observations on instrumental variable methods of time-series analysis , 1976 .

[3]  Keith Beven,et al.  Adaptive correction of deterministic models to produce probabilistic forecasts , 2012 .

[4]  Peter C. Young,et al.  Uncertainty , sensitivity analysis and the role of data based mechanistic modeling in hydrology , 2006 .

[5]  Peter C. Young,et al.  A data based mechanistic approach to nonlinear flood routing and adaptive flood level forecasting , 2008 .

[6]  Peter C. Young,et al.  Top‐down and data‐based mechanistic modelling of rainfall–flow dynamics at the catchment scale , 2003 .

[7]  Yuqiong Liu,et al.  Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework , 2007 .

[8]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[9]  Peter C Young,et al.  Advances in real–time flood forecasting , 2002, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[10]  R Shepherd,et al.  What determines trust in information about food-related risks? Underlying psychological constructs. , 1996, Risk analysis : an official publication of the Society for Risk Analysis.

[11]  Jaap Schellekens,et al.  The Delft-FEWS flow forecasting system , 2013, Environ. Model. Softw..

[12]  Kevin Horsburgh,et al.  Data‐based mechanistic modelling of tidally affected river reaches for flood warning purposes: an example on the River Dee, UK , 2013 .

[13]  P. Young,et al.  An optimal IV technique for identifying continuous-time transfer function model of multiple input systems , 2007 .

[14]  P. Young,et al.  Identification of non-linear stochastic systems by state dependent parameter estimation , 2001 .

[15]  Jaap Schellekens,et al.  Operational use of a grid-based model for flood forecasting , 2012 .

[16]  Keith Beven,et al.  Forecasting river levels during flash floods using Data Based Mechanistic models, online data assimilation and meteorological forecasts , 2010 .

[17]  Keith Beven,et al.  A staggered approach to flash flood forecasting – case study in the Cévennes region , 2011 .

[18]  R Symonds,et al.  Exercise Watermark: Improving the response to flooding in England and Wales , 2012 .

[19]  Keith Beven,et al.  Flash flood forecasting using Data-Based Mechanistic models and radar rainfall forecasts , 2012 .

[20]  Paul D. Bates,et al.  Modelling the hydraulics of the Carlisle 2005 flood event , 2010 .

[21]  Hugues Garnier,et al.  A new data-based modelling method for identifying parsimonious nonlinear rainfall/flow models , 2010 .

[22]  Diego J. Pedregal,et al.  Environmental time series analysis and forecasting with the Captain toolbox , 2007, Environ. Model. Softw..

[23]  P. Young,et al.  Simplicity out of complexity in environmental modelling: Occam's razor revisited. , 1996 .

[24]  Peter C. Young,et al.  Data-based mechanistic modelling and the rainfall-flow non-linearity. , 1994 .

[25]  P. Young,et al.  Data assimilation and adaptive forecasting of water levels in the river Severn catchment, United Kingdom , 2006 .

[26]  Keith Beven,et al.  On the colour and spin of epistemic error (and what we might do about it) , 2011 .

[27]  Peter C. Young,et al.  Identification and estimation of continuous-time, data-based mechanistic (DBM) models for environmental systems , 2006, Environ. Model. Softw..

[28]  Keith Beven,et al.  Application of data-based mechanistic modelling for flood forecasting at multiple locations in the Eden catchment in the National Flood Forecasting System (England and Wales) , 2013 .

[29]  Jaap Schellekens,et al.  Real-Time Geospatial Data Handling and Forecasting: Examples From Delft-FEWS Forecasting Platform/System , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.