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Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration
TLDR
The capability of the shuffled complex evolution automatic procedure is compared with the interactive multilevel calibration multistage semiautomated method developed for calibration of the Sacramento soil moisture accounting streamflow forecasting model of the U.S. National Weather Service and suggests that the state of the art in automatic calibration now can be expounded.
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 from
Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information
TLDR
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.
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 this
Artificial Neural Network Modeling of the Rainfall‐Runoff Process
An artificial neural network (ANN) is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. ANN models have been found
Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter
TLDR
Particle filters are introduced as a sequential Bayesian filtering having features that represent the full probability distribution of predictive uncertainties, and their applicability to the approximation of the posterior distribution of parameters is investigated.
Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?
TLDR
This paper compares a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling and demonstrates that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty.
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