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Effective and efficient global optimization for conceptual rainfall‐runoff models
The successful application of a conceptual rainfall-runoff (CRR) model depends on how well it is calibrated. Despite the popularity of CRR models, reports in the literature indicate that it isExpand
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A Modified Soil Adjusted Vegetation Index
There is currently a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science and globalExpand
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Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration
The usefulness of a hydrologic model depends on how well the model is calibrated. Expand
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Evaluation of PERSIANN system satellite-based estimates of tropical rainfall
Abstract PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall fromExpand
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Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information
This paper suggests that the emergence of a new and more powerful model calibration paradigm must include recognition of the inherent multiobjective nature of the problem and must explicitly recognize the role of model error. Expand
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Shuffled complex evolution approach for effective and efficient global minimization
The degree of difficulty in solving a global optimization problem is in general dependent on the dimensionality of the problem and certain characteristics of the objective function. This paperExpand
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A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters
Author(s): Vrugt, JA; Gupta, HV; Bouten, W; Sorooshian, S | Abstract: Markov Chain Monte Carlo (MCMC) methods have become increasingly popular for estimating the posterior probability distribution ofExpand
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Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks
Abstract A system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of thisExpand
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Optimal use of the SCE-UA global optimization method for calibrating watershed models
A global optimization method known as the SCE-UA (shuffled complex evolution method developed at The University of Arizona) has shown promise as an effective and efficient optimization technique for calibrating watershed models. Expand
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Dual state-parameter estimation of hydrological models using ensemble Kalman filter
A dual state–parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. Expand
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