Estimating the uncertain mathematical structure of a water balance model via Bayesian data assimilation

By N. Bulygina, Hoshin V. Gupta. 2009

90 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/3e95264b491261d00ea846b0645cb1031130e1cc

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In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Pursuing the method of multiple working hypotheses for hydrological modeling (2011, methodology): “…to directly describe system nonlinearities and data uncertainties [e.g., Kavetski et al., 2002; Vrugt et al., 2008; Cressie et al., 2009; Hsu et al., 2009; Renard et al., 2010; and many others], while exploiting independent process understanding as prior knowledge [e.g., Bulygina and Gupta, 2009].”
A multiobjective response surface approach for improved water quality planning in lakes and reservoirs (2010, background): “A great research effort has been directed in recent years toward this target [e.g., Yilmaz et al., 2008; Bulygina and Gupta, 2009]: in fact the need for reducing structural model uncertainty is obviously independent of the use of the LPRS procedure but affects any decision‐making process based on mo”
Towards a comprehensive assessment of model structural adequacy (2012, methodology): “Alternatively, the Bayesian Estimation of Structure approach [Bulygina and Gupta, 2009, 2010, 2011] seeks to use data to refine all of the equations in a model, not just those associated with streamflow recessions.”
Correcting the mathematical structure of a hydrological model via Bayesian data assimilation (2011, methodology): “[8] This paper extends upon our previous work [Bulygina and Gupta, 2009; Bulygina and Gupta, 2010] by developing a connection between BESt and the method of data augmentation [Tanner and Wong, 1987], thereby allowing statements to be made about convergence of the method.”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, methodology): “Recently, a new method called the Bayesian estimation of structure (BESt) approach [Bulygina and Gupta, 2009, 2010, 2011] has been proposed to resolve the underlying structure of the model via data assimilation conducted on the raw data.”
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors (2010, methodology): “Formulate the CRR model itself as a joint probability density function [Bulygina and Gupta, 2009].”
Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination (2012, methodology): “Studies have also focused on alternative approaches to address structural deficiencies without resorting to model combination [Kuczera et al., 2006; Huard and Mailhot, 2008; Bulygina and Gupta, 2009; Reichert and Mieleitner, 2009].”