Uncertainty Quantification of Hydrologic Predictions and Risk Analysis
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[1] D. McLaughlin,et al. Hydrologic Data Assimilation with the Ensemble Kalman Filter , 2002 .
[2] Y. P. Li,et al. Planning Regional Water Resources System Using an Interval Fuzzy Bi-Level Programming Method , 2010 .
[3] Fangjian Wang,et al. An Improved Particle Filter Algorithm Based on Ensemble Kalman Filter and Markov Chain Monte Carlo Method , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[4] B. Rémillard,et al. Goodness-of-fit tests for copulas: A review and a power study , 2006 .
[5] Claude E. Shannon,et al. A Mathematical Theory of Communications , 1948 .
[6] Amir AghaKouchak,et al. Entropy–Copula in Hydrology and Climatology , 2014 .
[7] Masson-Delmotte,et al. The Physical Science Basis , 2007 .
[8] T. Nakayama,et al. Impact of the Three-Gorges Dam and water transfer project on Changjiang floods , 2013 .
[9] S. Sorooshian,et al. Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .
[10] C. Diks,et al. Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation , 2005 .
[11] D. Darling,et al. A Test of Goodness of Fit , 1954 .
[12] Karl Iagnemma,et al. A polynomial chaos approach to the analysis of vehicle dynamics under uncertainty , 2012 .
[13] Zongxue Xu,et al. Risk estimation for flood and drought: case studies , 2001 .
[14] .. M.Arshad,et al. Anderson Darling and Modified Anderson Darling Tests for Generalized Pareto Distribution , 2003 .
[15] D. Madigan,et al. Bayesian Model Averaging for Linear Regression Models , 1997 .
[16] Habib N. Najm,et al. Stochastic spectral methods for efficient Bayesian solution of inverse problems , 2005, J. Comput. Phys..
[17] Erwin Zehe,et al. Temporal dynamics of model parameter sensitivity for computationally expensive models with the Fourier amplitude sensitivity test , 2011 .
[18] J. Vrugt,et al. Approximate Bayesian Computation using Markov Chain Monte Carlo simulation: DREAM(ABC) , 2014 .
[19] Shenglian Guo,et al. Flood Coincidence Risk Analysis Using Multivariate Copula Functions , 2012, Springer Water.
[20] P. Gelder,et al. Forecasting daily streamflow using hybrid ANN models , 2006 .
[21] Neil J. Gordon,et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..
[22] Vijay P. Singh,et al. Single‐site monthly streamflow simulation using entropy theory , 2011 .
[23] N. Wiener. The Homogeneous Chaos , 1938 .
[24] G. Evensen. Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .
[25] Jasper A. Vrugt,et al. Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts and applications (online first) , 2012 .
[26] K. Burnham,et al. Model selection: An integral part of inference , 1997 .
[27] S. Sorooshian,et al. A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters , 2002 .
[28] Daoyong Yang,et al. Simultaneous Estimation of Relative Permeability and Capillary Pressure Using Ensemble-Based History Matching Techniques , 2012, Transport in Porous Media.
[29] P. E. O'connell,et al. An introduction to the European Hydrological System — Systeme Hydrologique Europeen, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system , 1986 .
[30] Shenglian Guo,et al. Drought Analysis Using Copulas , 2013, Springer Water.
[31] C. Genest,et al. Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask , 2007 .
[32] Vijay P. Singh,et al. The NWS River Forecast System - catchment modeling. , 1995 .
[33] Liu Zhi-yu,et al. Trends of Extreme Flood Events in the Pearl River Basin during 1951–2010 , 2013 .
[34] S. P. Simonovic,et al. Bivariate flood frequency analysis: Part 1. Determination of marginals by parametric and nonparametric techniques , 2008 .
[35] H. Kunsch,et al. Bridging the ensemble Kalman and particle filters , 2012, 1208.0463.
[36] Chang’an Li,et al. Human impact on floods and flood disasters on the Yangtze River , 2001 .
[37] Mitja Brilly,et al. Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River , 2015 .
[38] 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 .
[39] P. Farrell,et al. Comprehensive study of tests for normality and symmetry: extending the Spiegelhalter test , 2006 .
[40] Sheng Yue,et al. A bivariate gamma distribution for use in multivariate flood frequency analysis , 2001 .
[41] S. Hagemann,et al. Climate change impact on available water resources obtained using multiple global climate and hydrology models , 2012 .
[42] Seong Jin Noh,et al. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities , 2012 .
[43] Andrew W. Western,et al. Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags , 2013 .
[44] Soroosh Sorooshian,et al. General Review of Rainfall-Runoff Modeling: Model Calibration, Data Assimilation, and Uncertainty Analysis , 2009 .
[45] Adrian E. Raftery,et al. Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .
[46] K. Beven. Rainfall-Runoff Modelling: The Primer , 2012 .
[47] R. Nelsen. An Introduction to Copulas , 1998 .
[48] Renaud Hostache,et al. Propagation of uncertainties in coupled hydro-meteorological forecasting systems: a stochastic approach for the assessment of the total predictive uncertainty. , 2011 .
[49] P. Fearnhead,et al. On‐line inference for hidden Markov models via particle filters , 2003 .
[50] C. T. Haan,et al. Statistical Methods In Hydrology , 1977 .
[51] Bernard Bobée,et al. Towards a systematic approach to comparing distributions used in flood frequency analysis , 1993 .
[52] P. Ciais,et al. The impacts of climate change on water resources and agriculture in China , 2010, Nature.
[53] J. N. Kapur,et al. Entropy optimization principles with applications , 1992 .
[54] Faisal Hossain,et al. Hydrological Risk Assessment of Old Dams: Case Study on Wilson Dam of Tennessee River Basin , 2012 .
[55] Rao S. Govindaraju,et al. A copula-based joint deficit index for droughts. , 2010 .
[56] Breanndán Ó Nualláin,et al. Parameter optimisation and uncertainty assessment for large-scale streamflow simulation with the LISFLOOD model , 2007 .
[57] Vijay P. Singh,et al. Hydrologic Synthesis Using Entropy Theory: Review , 2011 .
[58] V. Singh,et al. Bivariate Flood Frequency Analysis Using the Copula Method , 2006 .
[59] Vijay P. Singh,et al. A Multivariate Stochastic Flood Analysis Using Entropy , 1987 .
[60] G. Lannoy,et al. The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter , 2011 .
[61] V. Singh,et al. Entropy-based assessment and clustering of potential water resources availability , 2005 .
[62] F. Ludwig,et al. Global water resources affected by human interventions and climate change , 2013, Proceedings of the National Academy of Sciences.
[63] Soroosh Sorooshian,et al. Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods , 2000 .
[64] Hamid Moradkhani,et al. Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting , 2012 .
[65] Daoyong Yang,et al. Estimation of relative permeability and capillary pressure for tight formations by assimilating field production data , 2014 .
[66] Andrea Petroselli,et al. Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation , 2012 .
[67] Vijay P. Singh,et al. Bivariate rainfall frequency distributions using Archimedean copulas , 2007 .
[68] Yuguo Chen,et al. State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter , 2009 .
[69] Taha B. M. J. Ouarda,et al. The Gumbel mixed model for flood frequency analysis , 1999 .
[70] George Kuczera,et al. Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors , 2010 .
[71] R. Reichle. Data assimilation methods in the Earth sciences , 2008 .
[72] Guohe Huang,et al. Incorporation of Inexact Dynamic Optimization with Fuzzy Relation Analysis for Integrated Climate Change Impact Study , 1996 .
[73] C. Cunnane. Statistical distributions for flood frequency analysis , 1989 .
[74] Pierre F. J. Lermusiaux,et al. Data Assimilation with Gaussian Mixture Models Using the Dynamically Orthogonal Field Equations. Part I: Theory and Scheme , 2013 .
[75] Renzo Rosso,et al. Bivariate Statistical Approach to Check Adequacy of Dam Spillway , 2005 .
[76] S. Simonovic,et al. Bivariate flood frequency analysis. Part 2: a copula‐based approach with mixed marginal distributions , 2009 .
[77] P. Mantovan,et al. Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology , 2006 .
[78] Conglin Wu,et al. Hydrological Predictions: Using Data-Driven Models Coupled with Data Preprocessing Techniques , 2011 .
[79] M. Clark,et al. Operational hydrological data assimilation with the recursive ensemble Kalman filter , 2013 .
[80] Soroosh Sorooshian,et al. Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method , 2012 .
[81] Zhao Ren-jun,et al. The Xinanjiang model applied in China , 1992 .
[82] Daoyong Yang,et al. Simultaneous estimation of relative permeability and capillary pressure for tight formations using ensemble-based history matching method , 2013 .
[83] Robin De Keyser,et al. Improving particle filters in rainfall‐runoff models: Application of the resample‐move step and the ensemble Gaussian particle filter , 2013 .
[84] Ibrahim Hoteit,et al. Robust Ensemble Filtering and Its Relation to Covariance Inflation in the Ensemble Kalman Filter , 2011, 1108.0158.
[85] Keith Beven,et al. The Institute of Hydrology distributed model , 1987 .
[86] Wei Li,et al. Inexact fuzzy two-stage programming for water resources management in an environment of fuzziness and randomness , 2012, Stochastic Environmental Research and Risk Assessment.
[87] Konstantine P. Georgakakos,et al. A generalized stochastic hydrometeorological model for flood and flash‐flood forecasting: 1. Formulation , 1986 .
[88] Dennis McLaughlin,et al. An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering , 2002 .
[89] Yi Zheng,et al. Uncertainty assessment for watershed water quality modeling: A Probabilistic Collocation Method based approach , 2011 .
[90] D. Xiu,et al. Modeling uncertainty in flow simulations via generalized polynomial chaos , 2003 .
[91] Tong Jiang,et al. Hydrological modeling of River Xiangxi using SWAT2005: A comparison of model parameterizations using station and gridded meteorological observations , 2010 .
[92] Dong-Jun Seo,et al. The distributed model intercomparison project (DMIP): Motivation and experiment design , 2004 .
[93] David R. Anderson,et al. Model selection and multimodel inference : a practical information-theoretic approach , 2003 .
[94] Qinghua Cai,et al. The influence of topography and land use on water quality of Xiangxi River in Three Gorges Reservoir region , 2009 .
[95] Yuqiong Liu,et al. Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework , 2007 .
[96] K. Beven,et al. A physically based, variable contributing area model of basin hydrology , 1979 .
[97] Jery R. Stedinger,et al. Water Resources Systems Planning And Management , 2006 .
[98] R. E. Kalman,et al. A New Approach to Linear Filtering and Prediction Problems , 2002 .
[99] E. J. Gumbel,et al. Statistics of Extremes. , 1960 .
[100] Y. Tachikawa,et al. Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization , 2011 .
[101] Fateh Chebana,et al. Exploratory functional flood frequency analysis and outlier detection , 2012 .
[102] F. Massey. The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .
[103] Vijay P. Singh,et al. Meta-elliptical copulas for drought frequency analysis of periodic hydrologic data , 2010 .
[104] Taesam Lee,et al. Copula-based stochastic simulation of hydrological data applied to Nile River flows , 2011 .
[105] J. M. Bates,et al. The Combination of Forecasts , 1969 .
[106] Niko E. C. Verhoest,et al. Fitting bivariate copulas to the dependence structure between storm characteristics: A detailed analysis based on 105 year 10 min rainfall , 2010 .
[107] M. Stephens,et al. K-Sample Anderson–Darling Tests , 1987 .
[108] George Kuczera,et al. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm , 1998 .
[109] Hoshin Vijai Gupta,et al. Model identification for hydrological forecasting under uncertainty , 2005 .
[110] Jasper A. Vrugt,et al. Semi-distributed parameter optimization and uncertainty assessment for large-scale streamflow simulation using global optimization / Optimisation de paramètres semi-distribués et évaluation de l'incertitude pour la simulation de débits à grande échelle par l'utilisation d'une optimisation globale , 2008 .
[111] A. W. Heemink,et al. The ensemble particle filter (EnPF) in rainfall-runoff models , 2009 .
[112] Francesco Serinaldi,et al. Asymmetric copula in multivariate flood frequency analysis , 2006 .
[113] André St-Hilaire,et al. A nested multivariate copula approach to hydrometeorological simulations of spring floods: the case of the Richelieu River (Québec, Canada) record flood , 2014, Stochastic Environmental Research and Risk Assessment.
[114] V. Singh,et al. THE USE OF ENTROPY IN HYDROLOGY AND WATER RESOURCES , 1997 .