Real‐Time Updating in Flood Forecasting and Warning

[1]  A. O'Hagan,et al.  Gaussian process emulation of dynamic computer codes , 2009 .

[2]  Peter C. Young,et al.  Reduced order emulation of distributed hydraulic models , 2009 .

[3]  Dong Jun Seo,et al.  Automatic state updating for operational streamflow forecasting via variational data assimilation , 2009 .

[4]  Robert J. Moore,et al.  Hydrological modelling using raingauge- and radar-based estimators of areal rainfall , 2008 .

[5]  Xiangjun Tian,et al.  A land surface soil moisture data assimilation system based on the dual‐UKF method and the Community Land Model , 2008 .

[6]  Peter C. Young,et al.  The refined instrumental variable method , 2008 .

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

[8]  Peter C. Young,et al.  State Dependent Parameter metamodelling and sensitivity analysis , 2007, Comput. Phys. Commun..

[9]  R. Ibbitt,et al.  Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model , 2007 .

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

[11]  M. B. Beck,et al.  On the identification of model structure in hydrological and environmental systems , 2007 .

[12]  R. Moore.  The PDM rainfall-runoff model , 2007 .

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

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

[15]  Keith Beven,et al.  A manifesto for the equifinality thesis , 2006 .

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

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

[18]  C. Diks,et al.  Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation , 2005 .

[19]  M. B. Beck,et al.  On the development and application of a continuous-discrete recursive prediction error algorithm. , 2004, Mathematical biosciences.

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

[21]  Dong-Jun Seo,et al.  Real-Time Variational Assimilation of Hydrologic and Hydrometeorological Data into Operational Hydrologic Forecasting , 2003 .

[22]  Neil McIntyre,et al.  Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis , 2003 .

[23]  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.

[24]  M. Trosset,et al.  Bayesian recursive parameter estimation for hydrologic models , 2001 .

[25]  Keith Beven,et al.  Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology , 2001 .

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

[27]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[28]  Peter C. Young,et al.  Data-based mechanistic modelling, generalised sensitivity and dominant mode analysis , 1999 .

[29]  Jens Christian Refsgaard,et al.  Validation and Intercomparison of Different Updating Procedures for Real-Time Forecasting , 1997 .

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

[31]  A. Jakeman,et al.  Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments , 1990 .

[32]  R. Moore.  The probability-distributed principle and runoff production at point and basin scales , 1985 .

[33]  Lennart Ljung,et al.  The Extended Kalman Filter as a Parameter Estimator for Linear Systems , 1979 .

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

[35]  P. Young.  An instrumental variable method for real-time identification of a noisy process , 1970 .

[36]  H. Kushner.  Dynamical equations for optimal nonlinear filtering , 1967 .

[37]  R. Kopp,et al.  LINEAR REGRESSION APPLIED TO SYSTEM IDENTIFICATION FOR ADAPTIVE CONTROL SYSTEMS , 1963 .

[38]  R. Plackett.  Some theorems in least squares. , 1950, Biometrika.

[39]  Peter C. Young,et al.  Reduced Order Emulation of Distributed Hydraulic Simulation Models , 2009 .

[40]  Peter C. Young,et al.  Computationally efficient flood water level prediction (with uncertainty). , 2009 .

[41]  Peter C. Young,et al.  Data-based mechanistic modelling and river flow forecasting , 2006 .

[42]  Peter C. Young,et al.  The Identification and Estimation of Nonlinear Stochastic Systems , 2001 .

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

[44]  P. Young,et al.  Time variable and state dependent modelling of non-stationary and nonlinear time series , 1993 .

[45]  P. Young,et al.  Refined instrumental variable methods of recursive time-series analysis Part I. Single input, single output systems , 1979 .

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

[47]  P. Young,et al.  Hydrology and Earth System Sciences Discussions Uncertainty, Sensitivity Analysis and the Role of Data Based Mechanistic Modeling in Hydrology , 2022 .