Data‐based mechanistic modelling of tidally affected river reaches for flood warning purposes: an example on the River Dee, UK

This article discusses a coupled water level forecasting system constructed for the River Dee (UK) using parsimonious, physically interpretable, time series models. Tidal forecasts, provided by a simple harmonic model, and observed water levels at the upstream boundary are used to drive a nonlinear hydrological model which forecasts water levels at three gauged sites on the flood plain. The assimilation of observed data and use of the model for real-time forecasting is presented. The results generated indicate that the forecasts of river water can be both timely and accurate except close to the tidal boundary where the the tide is affected by the weir at Chester.

[1]  Florian Pappenberger,et al.  Grasping the unavoidable subjectivity in calibration of flood inundation models: A vulnerability weighted approach , 2007 .

[2]  Wade T. Crow,et al.  An adaptive ensemble Kalman filter for soil moisture data assimilation , 2007 .

[3]  Fred C. Schweppe,et al.  Evaluation of likelihood functions for Gaussian signals , 1965, IEEE Trans. Inf. Theory.

[4]  Peter C. Young,et al.  Data-based mechanistic modelling and the simplification of environmental systems. , 2004 .

[5]  Hugues Garnier,et al.  Refined instrumental variable methods for identification of LPV Box-Jenkins models , 2010, Autom..

[6]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[7]  Keith Beven,et al.  Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data , 1998 .

[8]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[9]  Marco Borga,et al.  Adaptive Use of a Conceptual Model for Real Time Flood Forecasting , 1997 .

[10]  Florian Pappenberger,et al.  A data assimilation approach to discharge estimation from space , 2009 .

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

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

[13]  Peter C. Young,et al.  Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. , 1998 .

[14]  Peter C. Young,et al.  Recursive Estimation and Time-Series Analysis: An Introduction , 1984 .

[15]  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.

[16]  Delwyn Moller,et al.  Estimation of bathymetric depth and slope from data assimilation of swath altimetry into a hydrodynamic model , 2008 .

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

[18]  Keith Beven,et al.  Detection of structural inadequacy in process‐based hydrological models: A particle‐filtering approach , 2008 .

[19]  Peter C. Young,et al.  Recursive and en-bloc approaches to signal extraction , 1999 .

[20]  K. Beven,et al.  Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations , 2005 .

[21]  Henrik Madsen,et al.  Adaptive state updating in real-time river flow forecasting—a combined filtering and error forecasting procedure , 2005 .

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

[23]  Soroosh Sorooshian,et al.  Dual state-parameter estimation of hydrological models using ensemble Kalman filter , 2005 .

[24]  P. Bates,et al.  A technique for the calibration of hydraulic models using uncertain satellite observations of flood extent , 2009 .

[25]  P. Bates,et al.  Reach scale floodplain inundation dynamics observed using airborne synthetic aperture radar imagery: Data analysis and modelling , 2006 .

[26]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[27]  P. Young,et al.  A systems approach to recursive economic forecasting and seasonal adjustment , 1989 .

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

[29]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[30]  G. Lannoy,et al.  Assimilating SAR-derived water level data into a hydraulic model: A case study , 2011 .

[31]  Kuolin Hsu,et al.  Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter , 2005 .

[32]  D. Pugh.  Tides, Surges and Mean Sea-Level , 1987 .

[33]  Harihar Rajaram,et al.  Recursive parameter estimation of hydrologic models , 1989 .

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

[35]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

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

[37]  A. Weerts,et al.  Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models , 2006 .

[38]  A. Piacentini,et al.  Correction of upstream flow and hydraulic state with data assimilation in the context of flood forecasting , 2010 .