Approximate Bayesian Computation in hydrologic modeling: equifinality of formal and informal approaches
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[1] Hubert H. G. Savenije,et al. A comparison of alternative multiobjective calibration strategies for hydrological modeling , 2007 .
[2] 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.
[3] Arthur Kosowsky,et al. Fast cosmological parameter estimation from microwave background temperature and polarization power spectra , 2004 .
[4] Cajo J. F. ter Braak,et al. Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation , 2008 .
[5] Lars-Christer Lundin,et al. Equifinality and sensitivity in freezing and thawing simulations of laboratory and in situ data , 2006 .
[6] D. Kavetski,et al. Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters , 2006 .
[7] B. Troutman. Errors and Parameter Estimation in Precipitation‐Runoff Modeling: 1. Theory , 1985 .
[8] Paul Marjoram,et al. Statistical Applications in Genetics and Molecular Biology Approximately Sufficient Statistics and Bayesian Computation , 2011 .
[9] Kuolin Hsu,et al. Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter , 2005 .
[10] P. Mantovan,et al. Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology , 2006 .
[11] Keith Beven,et al. A manifesto for the equifinality thesis , 2006 .
[12] G. Kuczera. Improved parameter inference in catchment models: 2. Combining different kinds of hydrologic data and testing their compatibility , 1983 .
[13] A. Brath,et al. A stochastic approach for assessing the uncertainty of rainfall‐runoff simulations , 2004 .
[14] Jasper A. Vrugt,et al. High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high‐performance computing , 2012 .
[15] J. Kaipio,et al. Compensation of errors due to discretization, domain truncation and unknown contact impedances in electrical impedance tomography , 2009 .
[16] K. Beven,et al. Comment on “Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?” by Jasper A. Vrugt, Cajo J. F. ter Braak, Hoshin V. Gupta and Bruce A. Robinson , 2009 .
[17] George Kuczera,et al. Calibration of conceptual hydrological models revisited: 1. Overcoming numerical artefacts , 2006 .
[18] Bruce A. Robinson,et al. Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces , 2009, IEEE Transactions on Evolutionary Computation.
[19] Mark M. Tanaka,et al. Sequential Monte Carlo without likelihoods , 2007, Proceedings of the National Academy of Sciences.
[20] J. Nash,et al. River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .
[21] J. Stedinger,et al. Appraisal of the generalized likelihood uncertainty estimation (GLUE) method , 2008 .
[22] R. Ibbitt,et al. Effects of random data errors on the parameter values for a conceptual model , 1972 .
[23] Soroosh Sorooshian,et al. Toward improved identifiability of hydrologic model parameters: The information content of experimental data , 2002 .
[24] D. Balding,et al. Approximate Bayesian computation in population genetics. , 2002, Genetics.
[25] G. Kuczera. Improved parameter inference in catchment models: 1. Evaluating parameter uncertainty , 1983 .
[26] Arnaud Doucet,et al. An adaptive sequential Monte Carlo method for approximate Bayesian computation , 2011, Statistics and Computing.
[27] B. Bates,et al. A Markov Chain Monte Carlo Scheme for parameter estimation and inference in conceptual rainfall‐runoff modeling , 2001 .
[28] Eero Saksman,et al. Adaptive proposal distribution for random walkMetropolis , 1999 .
[29] Peter Reichert,et al. Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time‐dependent parameters , 2009 .
[30] T. Gasser,et al. Nonparametric estimation of residual variance revisited , 1993 .
[31] Peter C. Young,et al. Hypothetico‐inductive data‐based mechanistic modeling of hydrological systems , 2013 .
[32] Paul D. Bates,et al. Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE , 2002 .
[33] Dimitri Solomatine,et al. A novel method to estimate model uncertainty using machine learning techniques , 2009 .
[34] V. Pauwels. A multistart weight‐adaptive recursive parameter estimation method , 2008 .
[35] Lucy Marshall,et al. Towards dynamic catchment modelling: a Bayesian hierarchical mixtures of experts framework , 2007 .
[36] Soroosh Sorooshian,et al. Comment on: Bayesian recursive parameter estimation for hydrologic models. Authors' reply , 2003 .
[37] Tyler Smith,et al. Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments , 2010 .
[38] George Kuczera,et al. Semidistributed hydrological modeling: A “saturation path” perspective on TOPMODEL and VIC , 2003 .
[39] John R. Williams,et al. SENSITIVITY AND UNCERTAINTY ANALYSES OF CROP YIELDS AND SOIL ORGANIC CARBON SIMULATED WITH EPIC , 2005 .
[40] D. Higdon,et al. Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling , 2009 .
[41] J. Seibert. Multi-criteria calibration of a conceptual runoff model using a genetic algorithm , 2000 .
[42] Jasper A Vrugt,et al. Improved evolutionary optimization from genetically adaptive multimethod search , 2007, Proceedings of the National Academy of Sciences.
[43] Johan Alexander Huisman,et al. Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments , 2012 .
[44] Brandon M. Turner,et al. Journal of Mathematical Psychology a Tutorial on Approximate Bayesian Computation , 2022 .
[45] Q. J. Wang. THE GENETIC ALGORITHM AND ITS APPLICAYTION TO CALIBRATING CONCEPUTAL RAINFALL-RUNOFF MODELS , 1991 .
[46] S. Sorooshian,et al. Automatic calibration of conceptual rainfall-runoff models: The question of parameter observability and uniqueness , 1983 .
[47] J. Vrugt,et al. Corruption of accuracy and efficiency of Markov chain Monte Carlo simulation by inaccurate numerical implementation of conceptual hydrologic models , 2010 .
[48] S. Sorooshian,et al. Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data , 1996 .
[49] Bryan A. Tolson,et al. Dynamically dimensioned search algorithm for computationally efficient watershed model calibration , 2007 .
[50] Henrik Madsen,et al. Including prior information in the estimation of effective soil parameters in unsaturated zone modelling , 2004 .
[51] Soroosh Sorooshian,et al. Dual state-parameter estimation of hydrological models using ensemble Kalman filter , 2005 .
[52] S. Sorooshian,et al. Stochastic parameter estimation procedures for hydrologie rainfall‐runoff models: Correlated and heteroscedastic error cases , 1980 .
[53] Christophe Andrieu,et al. Model criticism based on likelihood-free inference, with an application to protein network evolution , 2009, Proceedings of the National Academy of Sciences.
[54] K. Beven,et al. Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE Approach , 1996 .
[55] Keith Beven,et al. On the sensitivity of soil-vegetation-atmosphere transfer (SVAT) schemes: equifinality and the problem of robust calibration , 1997 .
[56] Soroosh Sorooshian,et al. Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information , 1998 .
[57] Keith Beven,et al. The future of distributed models: model calibration and uncertainty prediction. , 1992 .
[58] Keith Beven,et al. So just why would a modeller choose to be incoherent , 2008 .
[59] P. Hall,et al. Asymptotically optimal difference-based estimation of variance in nonparametric regression , 1990 .
[60] Qingyun Duan,et al. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction , 2006 .
[61] C. Diks,et al. Improved treatment of uncertainty in hydrologic modeling , 2004 .
[62] George Kuczera,et al. Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation , 2011 .
[63] George Kuczera,et al. Bayesian analysis of input uncertainty in hydrological modeling: 2. Application , 2006 .
[64] C. Robert,et al. ABC likelihood-free methods for model choice in Gibbs random fields , 2008, 0807.2767.
[65] George Kuczera,et al. On the relationship between the reliability of parameter estimates and hydrologic time series data used in calibration , 1982 .
[66] Neil McIntyre,et al. Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis , 2003 .
[67] Alberto Montanari,et al. Estimating the uncertainty of hydrological forecasts: A statistical approach , 2008 .
[68] J. Vrugt,et al. Toward diagnostic model calibration and evaluation: Approximate Bayesian computation , 2013 .
[69] Bruce A. Robinson,et al. Response to comment by Keith Beven on “Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?” , 2009 .
[70] Keith Beven,et al. Detection of structural inadequacy in process‐based hydrological models: A particle‐filtering approach , 2008 .
[71] Soroosh Sorooshian,et al. Multi-objective global optimization for hydrologic models , 1998 .
[72] Jasper A. Vrugt,et al. Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts and applications (online first) , 2012 .
[73] Emmanouil N. Anagnostou,et al. A Statistical Approach to Ground Radar-Rainfall Estimation , 2005 .
[74] S. Sorooshian,et al. Evaluation of Maximum Likelihood Parameter estimation techniques for conceptual rainfall‐runoff models: Influence of calibration data variability and length on model credibility , 1983 .
[75] Keith Beven,et al. Prophecy, reality and uncertainty in distributed hydrological modelling , 1993 .
[76] M. Clark,et al. Snow Data Assimilation via an Ensemble Kalman Filter , 2006 .
[77] Cajo J. F. ter Braak,et al. Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? , 2009 .
[78] J. Vrugt,et al. A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors , 2010 .
[79] Hamid Moradkhani,et al. Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting , 2012 .
[80] M. Clark,et al. Probabilistic Quantitative Precipitation Estimation in Complex Terrain , 2005 .
[81] H. Gupta,et al. Correcting the mathematical structure of a hydrological model via Bayesian data assimilation , 2011 .
[82] Steen Christensen. A synthetic groundwater modelling study of the accuracy of GLUE uncertainty intervals , 2002 .
[83] Misgana K. Muleta,et al. Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model , 2005 .
[84] Knut-Andreas Lie,et al. Adaptive Multiscale Streamline Simulation and Inversion for High-Resolution Geomodels , 2008 .
[85] David J. Nott,et al. Generalized likelihood uncertainty estimation (GLUE) and approximate Bayesian computation: What's the connection? , 2012 .
[86] Asaad Y. Shamseldin,et al. Development of a possibilistic method for the evaluation of predictive uncertainty in rainfall‐runoff modeling , 2007 .
[87] L. Feyen,et al. Assessing parameter, precipitation, and predictive uncertainty in a distributed hydrological model using sequential data assimilation with the particle filter , 2009 .
[88] Keith Beven,et al. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology , 2001 .