Title: Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework
Author: Smith, Tyler Jon; Marshall, Lucy Amanda.;
Source: Environmental Modelling and Software. 25: 691-701.
Publication Series: Scientific Journal (JRNL)
Description: Model selection is an extremely important aspect of many hydrologic modeling studies because of the complexity, variability, and uncertainty that surrounds the current understanding of watershed-scale systems. However, development and implementation of a complete precipitation-runoff modeling framework, from model selection to calibration and uncertainty analysis, are rarely confronted. This paper introduces a modular precipitation-runoff modeling framework that has been developed and applied to a research site in Central Montana, USA. The case study focuses on an approach to hydrologic modeling that considers model development, selection, calibration, uncertainty analysis, and overall assessment. The results of this case study suggest that a modular framework is useful in identifying the interactions between and among different process representations and their resultant predictions of stream discharge. Such an approach can strengthen model building and address an oft ignored aspect of predictive uncertainty; namely, model structural uncertainty.
Keywords: modeling framework, model comparison, Bayesian inference, Markov chain Monte Carlo simulation, calibration, uncertainty analysis, snowmelt, predictive performance
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Smith, Tyler Jon; Marshall, Lucy Amanda. 2010. Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework. Environmental Modelling and Software. 25: 691-701.
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