Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity
暂无分享,去创建一个
George Kuczera | Dmitri Kavetski | Mark Thyer | David McInerney | Guillaume Evin | D. Kavetski | G. Kuczera | M. Thyer | G. Évin | D. McInerney
[1] G. Kuczera. Improved parameter inference in catchment models: 1. Evaluating parameter uncertainty , 1983 .
[2] Peter Reichert,et al. Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time‐dependent parameters , 2009 .
[3] D. Kavetski,et al. Confronting Input Uncertainty in Environmental Modelling , 2013 .
[4] C. Perrin,et al. Improvement of a parsimonious model for streamflow simulation , 2003 .
[5] Bettina Schaefli,et al. Quantifying hydrological modeling errors through a mixture of normal distributions , 2007 .
[6] Dmitri Kavetski,et al. The open source RFortran library for accessing R from Fortran, with applications in environmental modelling , 2011, Environ. Model. Softw..
[7] George Kuczera,et al. Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation , 2011 .
[8] Alberto Montanari,et al. Estimating the uncertainty of hydrological forecasts: A statistical approach , 2008 .
[9] Keith Beven,et al. So just why would a modeller choose to be incoherent , 2008 .
[10] Q. Shao,et al. Experimental evaluation of the dynamic seasonal streamflow forecasting approach , 2011 .
[11] P. Mantovan,et al. Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology , 2006 .
[12] Soroosh Sorooshian,et al. Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops , 2006 .
[13] Demetris Koutsoyiannis,et al. A blueprint for process‐based modeling of uncertain hydrological systems , 2012 .
[14] Dmitri Kavetski,et al. Reply to comment by K. Beven et al. on “Pursuing the method of multiple working hypotheses for hydrological modeling” , 2012 .
[15] Alberto Montanari,et al. What do we mean by ‘uncertainty’? The need for a consistent wording about uncertainty assessment in hydrology , 2007 .
[16] Tyler Smith,et al. Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments , 2010 .
[17] Jery R. Stedinger,et al. Appraisal of the generalized likelihood uncertainty estimation (GLUE) method , 2008 .
[18] P. Reichert,et al. Linking statistical bias description to multiobjective model calibration , 2012 .
[19] Martyn P. Clark,et al. Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models , 2008 .
[20] Q. Duana,et al. Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops , 2006 .
[21] Dmitri Kavetski,et al. Pursuing the method of multiple working hypotheses for hydrological modeling , 2011 .
[22] George Kuczera,et al. Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis , 2009 .
[23] Stefania Tamea,et al. Verification tools for probabilistic forecasts of continuous hydrological variables , 2006 .
[24] J. Stedinger,et al. Appraisal of the generalized likelihood uncertainty estimation (GLUE) method , 2008 .
[25] George Kuczera,et al. Pitfalls and improvements in the joint inference of heteroscedasticity and autocorrelation in hydrological model calibration , 2013 .
[26] J. Vrugt,et al. A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors , 2010 .
[27] G. Villarini,et al. Empirically-based modeling of spatial sampling uncertainties associated with rainfall measurements by rain gauges , 2008 .
[28] George Kuczera,et al. Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors , 2010 .
[29] Dmitri Kavetski,et al. Ancient numerical daemons of conceptual hydrological modeling: 1. Fidelity and efficiency of time stepping schemes , 2010 .
[30] Keith Beven,et al. Comment on “Pursuing the method of multiple working hypotheses for hydrological modeling” by P. Clark et al. , 2012 .
[31] Benjamin Renard,et al. Evaluation of statistical models for forecast errors from the HBV model , 2010 .
[32] C. Perrin,et al. Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments , 2001 .
[33] S. Sorooshian,et al. Stochastic parameter estimation procedures for hydrologie rainfall‐runoff models: Correlated and heteroscedastic error cases , 1980 .
[34] K. Bogner,et al. Post‐processing hydrological ensemble predictions intercomparison experiment , 2013 .
[35] M. Clark,et al. Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction , 2010 .
[36] B. Bates,et al. A Markov Chain Monte Carlo Scheme for parameter estimation and inference in conceptual rainfall‐runoff modeling , 2001 .
[37] Chong-Yu Xu,et al. Systematic evaluation of autoregressive error models as post-processors for a probabilistic streamflow forecast system , 2011 .
[38] Anthony J. Jakeman,et al. Performance of conceptual rainfall‐runoff models in low‐yielding ephemeral catchments , 1997 .
[39] Khaled H. Hamed,et al. A modified Mann-Kendall trend test for autocorrelated data , 1998 .
[40] C. Diks,et al. Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation , 2005 .
[41] David R. Brillinger,et al. Consistent detection of a monotonic trend superposed on a stationary time series , 1989 .
[42] V. Singh,et al. The HBV model. , 1995 .
[43] Francesca Pianosi,et al. Dynamic modeling of predictive uncertainty by regression on absolute errors , 2012 .
[44] Patrick M. Reed,et al. Reducing uncertainty in predictions in ungauged basins by combining hydrologic indices regionalization and multiobjective optimization , 2008, Water Resources Research.
[45] Keith Beven,et al. The future of distributed models: model calibration and uncertainty prediction. , 1992 .
[46] J. Stedinger,et al. Multisite ARMA(1,1) and Disaggregation Models for Annual Streamflow Generation , 1985 .