A manifesto for the equifinality thesis

[1]  David Draper,et al.  Assessment and Propagation of Model Uncertainty , 2011 .

[2]  K Beven,et al.  On the concept of model structural error. , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[3]  Keith Beven,et al.  Data‐based modelling of runoff and chemical tracer concentrations in the Haute‐Mentue research catchment (Switzerland) , 2005 .

[4]  O. Cappé,et al.  Population Monte Carlo , 2004 .

[5]  P. Baveye The emergence of a new kind of relativism in environmental modelling: a commentary , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[6]  K. Beven Reply to ‘The emergence of a new kind of relativism in environmental modelling: a commentary’ by Philippe Baveye , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[7]  Keith Beven,et al.  Does an interagency meeting in Washington imply uncertainty? , 2004 .

[8]  Robert D. Brown Environmental Foresight and Models: A Manifesto , 2004 .

[9]  Ming Ye,et al.  Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff , 2003 .

[10]  Jim W. Hall,et al.  Handling uncertainty in the hydroinformatic process , 2003 .

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

[12]  Karsten Schulz,et al.  Data‐supported robust parameterisations in land surface–atmosphere flux predictions: towards a top‐down approach , 2003 .

[13]  Keith Beven,et al.  Vadose Zone Flow Model Uncertainty as Conditioned on Geophysical Data , 2003, Ground water.

[14]  S. Sorooshian,et al.  A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters , 2002 .

[15]  Soroosh Sorooshian,et al.  Toward improved identifiability of hydrologic model parameters: The information content of experimental data , 2002 .

[16]  Roman Krzysztofowicz,et al.  Bayesian system for probabilistic river stage forecasting , 2002 .

[17]  Keith Beven,et al.  Towards a coherent philosophy for environmental modelling. , 2002 .

[18]  K. Beven,et al.  A hydraulic model to predict drought-induced mortality in woody plants: an application to climate change in the Mediterranean. , 2002 .

[19]  Keith Beven,et al.  Flood frequency estimation by continuous simulation for a catchment treated as ungauged (with uncertainty) , 2002 .

[20]  Keith Beven,et al.  Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system , 2002 .

[21]  Jan Feyen,et al.  Constraining soil hydraulic parameter and output uncertainty of the distributed hydrological MIKE SHE model using the GLUE framework , 2002 .

[22]  Keith Beven,et al.  On constraining TOPMODEL hydrograph simulations using partial saturated area information , 2002 .

[23]  Peter C. Young,et al.  Observational data and scale‐dependent parameterizations: explorations using a virtual hydrological reality , 2002 .

[24]  J. Kirchner,et al.  Catchment-scale advection and dispersion as a mechanism for fractal scaling in stream tracer concentrations , 2001 .

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

[26]  K. Beven,et al.  Application of a data‐based mechanistic modelling (DBM) approach for predicting runoff generation in semi‐arid regions , 2001 .

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

[28]  Keith Beven,et al.  A dynamic TOPMODEL , 2001 .

[29]  Keith Beven,et al.  On hypothesis testing in hydrology , 2001 .

[30]  Keith Beven,et al.  Dalton Medal Lecture: How far can we go in distributed hydrological modelling? , 2001 .

[31]  Keith Beven,et al.  Stochastic capture zone delineation within the generalized likelihood uncertainty estimation methodology: Conditioning on head observations , 2001 .

[32]  Keith Beven,et al.  Flood frequency estimation by continuous simulation under climate change (with uncertainty) , 2000 .

[33]  K. Beven,et al.  Equifinality and uncertainty in physically based soil erosion models: Application of the glue methodology to WEPP-the water erosion prediction project-for sites in the UK and USA , 2000 .

[34]  Keith Beven,et al.  Uniqueness of place and process representations in hydrological modelling , 2000 .

[35]  Keith Beven,et al.  Equifinality and the problem of robust calibration in nitrogen budget simulations , 1999 .

[36]  Keith Beven,et al.  Equifinality, sensitivity and uncertainty in the estimation of critical loads. , 1999 .

[37]  George Kuczera,et al.  Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm , 1998 .

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

[39]  Keith Beven,et al.  Dynamic real-time prediction of flood inundation probabilities , 1998 .

[40]  Soroosh Sorooshian,et al.  Multi-objective global optimization for hydrologic models , 1998 .

[41]  Keith Beven,et al.  TOPMODEL : a critique. , 1997 .

[42]  K. Beven,et al.  Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE Approach , 1996 .

[43]  G. Kitagawa Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[44]  John Ewen,et al.  VALIDATION OF CATCHMENT MODELS FOR PREDICTING LAND-USE AND CLIMATE CHANGE IMPACTS. : 2. CASE STUDY FOR A MEDITERRANEAN CATCHMENT , 1996 .

[45]  Nong Shang,et al.  Parameter uncertainty and interaction in complex environmental models , 1994 .

[46]  N Oreskes,et al.  Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences , 1994, Science.

[47]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[48]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

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

[50]  M. B. Beck,et al.  Water quality modeling: A review of the analysis of uncertainty , 1987 .

[51]  Hong Wang,et al.  Recursive estimation and time-series analysis , 1986, IEEE Trans. Acoust. Speech Signal Process..

[52]  John H. Cushman,et al.  On Measurement, Scale, and Scaling , 1986 .

[53]  Peter C. Young,et al.  Water quality in river systems: Monte‐Carlo Analysis , 1979 .

[54]  R. Allan Freeze,et al.  Mathematical simulation of subsurface flow contributions to snowmelt runoff, Reynolds Creek Watershed, Idaho , 1974 .

[55]  R. Ibbitt,et al.  Fitting Methods for Conceptual Catchment Models , 1971 .

[56]  Frank Press,et al.  Earth models obtained by Monte Carlo inversion. , 1968 .

[57]  D. Cox,et al.  An Analysis of Transformations , 1964 .

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

[59]  Keith Beven,et al.  Investigating the Uncertainty in Predicting Responses to Atmospheric Deposition using the Model of Acidification of Groundwater in Catchments (MAGIC) within a Generalised Likelihood Uncertainty Estimation (GLUE) Framework , 2003 .

[60]  Peter Young,et al.  Data-based Mechanistic Modelling and Validation of Rainfall-flow Processes , 2001 .

[61]  Horritt,et al.  Model Validation: Perspectives in Hydrological Science , 2001 .

[62]  K. Beven Equifinality and Uncertainty in Geomorphological Modelling , 2001 .

[63]  M. G. Anderson,et al.  DATA-BASED MECHANISTIC MODELLING AND VALIDATION OF RAINFALL-FLOW PROCESSES , 2001 .

[64]  Keith Beven,et al.  Flood frequency estimation under climate change (with uncertainty). , 2000 .

[65]  Keith Beven,et al.  The use of generalised likelihood measures for uncertainty estimation in high order models of environmental systems , 2000 .

[66]  J. Ellis,et al.  Simplicity out of complexity , 2000, Nature.

[67]  L. Yang Fuzzy Logic with Engineering Applications , 1999 .

[68]  K. Bevenb,et al.  Use of spatially distributed water table observations to constrain uncertainty in a rainfall – runoff model , 1998 .

[69]  H. Piégay B. L. Rhoads and C. E. Thorn (Eds.) - The Scientific Nature of Geomorphology , 1998 .

[70]  Keith Beven,et al.  Prophecy, reality and uncertainty in distributed hydrological modelling , 1993 .

[71]  John D. Bredehoeft,et al.  Ground-water models cannot be validated , 1992 .

[72]  M. B. Beck,et al.  Uncertainty, identifiability and the propagation of prediction errors: A case study of lake ontario , 1991 .

[73]  Karel J. Keesman,et al.  Uncertainty propagation and speculation in projective forecasts of environmental change - a lake eutrophication example. , 1991 .

[74]  Peter C. Young,et al.  Recursive Estimation, Forecasting, and Adaptive Control , 1989 .

[75]  C. Howson,et al.  Scientific Reasoning: The Bayesian Approach , 1989 .

[76]  Y. I. Kim [General systems theory]. , 1989, Taehan kanho. The Korean nurse.

[77]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[78]  Peter C. Young,et al.  Recursive Estimation and Time Series Analysis , 1984 .

[79]  R. H. Gardner,et al.  Parameter Uncertainty and Model Predictions: A Review of Monte Carlo Results , 1983 .

[80]  Peter C. Young,et al.  The Validity and Credibility of Models for Badly Defined Systems , 1983 .

[81]  G. Hornberger,et al.  Approach to the preliminary analysis of environmental systems , 1981 .

[82]  G. Box An analysis of transformations (with discussion) , 1964 .