Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models

[1]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[2]  Hoshin Vijai Gupta,et al.  A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model , 2008 .

[3]  Yuqiong Liu,et al.  Reconciling theory with observations: elements of a diagnostic approach to model evaluation , 2008 .

[4]  P. Reed,et al.  Rainfall characteristics define the value of streamflow observations for distributed watershed model identification , 2008 .

[5]  P. Reed,et al.  Characterization of watershed model behavior across a hydroclimatic gradient , 2008 .

[6]  Thorsten Wagener,et al.  Numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox , 2007, Environ. Model. Softw..

[7]  Yong Tang,et al.  Parallelization strategies for rapid and robust evolutionary multiobjective optimization in water resources applications , 2007 .

[8]  Bryan A. Tolson,et al.  Dynamically dimensioned search algorithm for computationally efficient watershed model calibration , 2007 .

[9]  P. Reed,et al.  Hydrology and Earth System Sciences Discussions Comparing Sensitivity Analysis Methods to Advance Lumped Watershed Model Identification and Evaluation , 2022 .

[10]  A. Wood,et al.  Towards the systematic simplification of mechanistic models , 2006 .

[11]  Patrick M. Reed,et al.  Computational Scaling Analysis of Multiobjective Evolutionary Algorithms in Long-Term Groundwater Monitoring Applications , 2006 .

[12]  Soroosh Sorooshian,et al.  A 'User-Friendly' approach to parameter estimation in hydrologic models , 2006 .

[13]  Xu Liang,et al.  On the assessment of the impact of reducing parameters and identification of parameter uncertainties for a hydrologic model with applications to ungauged basins , 2006 .

[14]  Stephen J. Burges,et al.  Assessment of soil-based and calibrated parameters of the Sacramento model and parameter transferability , 2006 .

[15]  Thorsten Wagener,et al.  Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty , 2006 .

[16]  Patrick M. Reed,et al.  Reply to J. Vrugt's comment on "How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?" , 2007 .

[17]  Q. Duana,et al.  Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops , 2006 .

[18]  Patrick M. Reed,et al.  Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design , 2005 .

[19]  J. Doherty,et al.  A hybrid regularized inversion methodology for highly parameterized environmental models , 2005 .

[20]  Patrick M. Reed,et al.  The Value of Online Adaptive Search: A Performance Comparison of NSGAII, epsilon-NSGAII and epsilonMOEA , 2005, EMO.

[21]  Paul D. Bates,et al.  Distributed Sensitivity Analysis of Flood Inundation Model Calibration , 2005 .

[22]  Marco Laumanns,et al.  An Adaptive Scheme to Generate the Pareto Front Based on the Epsilon-Constraint Method , 2005, Practical Approaches to Multi-Objective Optimization.

[23]  S. Sorooshian,et al.  Effective and efficient algorithm for multiobjective optimization of hydrologic models , 2003 .

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

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

[26]  A. Saltelli,et al.  Making best use of model evaluations to compute sensitivity indices , 2002 .

[27]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[28]  E. Anderson,et al.  Calibration of Conceptual Hydrologic Models for Use in River Forecasting , 2002 .

[29]  Soroosh Sorooshian,et al.  Toward improved streamflow forecasts: value of semidistributed modeling , 2001 .

[30]  Soroosh Sorooshian,et al.  A framework for development and application of hydrological models , 2001, Hydrology and Earth System Sciences.

[31]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[32]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods , 2000 .

[33]  Victor Koren,et al.  Use of Soil Property Data in the Derivation of Conceptual Rainfall-Runoff Model Parameters , 2000 .

[34]  Soroosh Sorooshian,et al.  Sensitivity analysis of a land surface scheme using multicriteria methods , 1999 .

[35]  Fernando G. Lobo,et al.  A parameter-less genetic algorithm , 1999, GECCO.

[36]  Dong-Jun Seo,et al.  Scale dependencies of hydrologic models to spatial variability of precipitation , 1999 .

[37]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information , 1998 .

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

[39]  G. Kuczera.  Efficient subspace probabilistic parameter optimization for catchment models , 1997 .

[40]  Vijay P. Singh,et al.  The NWS River Forecast System - catchment modeling. , 1995 .

[41]  V. Singh,et al.  Computer Models of Watershed Hydrology , 1995 .

[42]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[43]  Soroosh Sorooshian,et al.  Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model , 1993 .

[44]  Ilya M. Sobol,et al.  Sensitivity Estimates for Nonlinear Mathematical Models , 1993 .

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

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