Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework
Y Liu, HV Gupta - Water resources research, 2007 - Wiley Online Library
Despite significant recent developments in computational power and distributed hydrologic
modeling, the issue of how to adequately address the uncertainty associated with …
modeling, the issue of how to adequately address the uncertainty associated with …
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities
Data assimilation (DA) holds considerable potential for improving hydrologic predictions as
demonstrated in numerous research studies. However, advances in hydrologic DA research …
demonstrated in numerous research studies. However, advances in hydrologic DA research …
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 …
identifying complex nonlinear relationships between input and output data sets. ANN …
Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information
HV Gupta, S Sorooshian… - Water Resources Research, 1998 - Wiley Online Library
Several contributions to the hydrological literature have brought into question the continued
usefulness of the classical paradigm for hydrologic model calibration. With the growing …
usefulness of the classical paradigm for hydrologic model calibration. With the growing …
Multi-objective global optimization for hydrologic models
PO Yapo, HV Gupta, S Sorooshian - Journal of hydrology, 1998 - Elsevier
The development of automated (computer-based) calibration methods has focused mainly
on the selection of a single-objective measure of the distance between the model-simulated …
on the selection of a single-objective measure of the distance between the model-simulated …
Dual state–parameter estimation of hydrological models using ensemble Kalman filter
Hydrologic models are twofold: models for understanding physical processes and models
for prediction. This study addresses the latter, which modelers use to predict, for example …
for prediction. This study addresses the latter, which modelers use to predict, for example …
Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter
Two elementary issues in contemporary Earth system science and engineering are (1) the
specification of model parameter values which characterize a system and (2) the estimation …
specification of model parameter values which characterize a system and (2) the estimation …
Calibration of rainfall‐runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model
Conceptual rainfall‐runoff models are difficult to calibrate by means of automatic methods;
one major reason for this is the inability of conventional procedures to locate the globally …
one major reason for this is the inability of conventional procedures to locate the globally …
An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction
The conventional treatment of uncertainty in rainfall‐runoff modeling primarily attributes
uncertainty in the input‐output representation of the model to uncertainty in the model …
uncertainty in the input‐output representation of the model to uncertainty in the model …
Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model
This paper describes an application of the ensemble Kalman filter (EnKF) in which
streamflow observations are used to update states in a distributed hydrological model. We …
streamflow observations are used to update states in a distributed hydrological model. We …