Our invited speaker is Lucy Marshall from the University of New South Wales, Sydney, Australia.
The application of multi-datasets and multi-objective functions has proven to improve the performance of hydrologic and water quality models by extracting complementary information from multiple data sources or multiple features of modelled variables. This is useful if more than one variable (runoff and snow cover, sediment or pollutant concentration) or more than one characteristic of the same variable (e.g., flood peaks and recession curves) are of interest.
Similarly, a multi-model approach can overcome shortcomings of individual models, while testing a model at multiple scales helps to improve our understanding of the model functioning in relation to catchment processes. Finally, the quantification of multiple uncertainty sources enables the identification of their individual contributions and this is critical for uncertainty reduction and decision making under uncertainty. In this aspect, Bayesian approaches emerge as very powerful tools for comprehensive handling of uncertainty in data, model structure and parameters.
This session welcomes contributions that apply one or more of the multi-aspects in hydrologic and water quality studies. In particular, we seek studies covering the following issues:
• Frameworks using multi-objectives or multi-variables to improve the identification (prediction) of hydrologic or water quality models
• Studies using multi-model or multiple-data-driven approaches
• Use of multiple scales or sites to improve understanding of catchment processes
• Assimilation of remote sensed data or use of multi-datasets to improve model identification
• Hypothesis testing with one of the multi-aspects
• Metaheuristics (e.g., Monte Carlo) or Bayesian approaches in combination with multi-aspects of model identification
• Techniques to optimize model calibration or uncertainty quantification via multi-aspect analyses
• Studies handling multiple uncertainty sources in a modelling framework
• Bayesian applications to address the problem of scaling (e.g. disparity between process, observations, model resolution and predictions) through hierarchical models
• Bayesian approaches to model water quality in data sparse environments
• Applications of Bayesian Belief Networks as decision support tools
• Application of machine learning and data mining approaches to learn from large, multiple or high-resolution data sets.
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