Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.

SSS7.7

Numerical models are flexible tools to describe fluxes of water, energy and matter in the groundwater-vadose zone-vegetation-atmosphere continuum. Even physically or process-based models rely on empirical parameters of unknown system properties and have to be determined by model-data-fusion at the scale of interest. Furthermore, state estimation methods are nowadays applied to update state variables and minimize model error. Joint state and parameter estimation methods have been developed to combine these two worlds. This session invites contributions on model-data-fusion in the vadose zone and its neighboring compartments with a focus on:
- improved methods to describe model error and prediction uncertainty,
- methods to disentangle parameter, forcing and model error,
- robust estimation techniques capable of handling non-Gaussian and correlated errors,
- assimilation of data from various sources,
- multi-model ensembles to improve uncertainty quantification,
- optimization of experiments and measurement campaigns in the lab and in the field to maximize information content and minimize uncertainty,
- model adequacy testing and model validation.
We are looking forward to receiving contributions which cover the whole range of scales: laboratory, field, pedon, and watershed scale.

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Co-organized as AS4.27/HS8.3.14
Convener: Tobias KD Weber  | Co-conveners: Sascha Iden , Efstathios Diamantopoulos 
Numerical models are flexible tools to describe fluxes of water, energy and matter in the groundwater-vadose zone-vegetation-atmosphere continuum. Even physically or process-based models rely on empirical parameters of unknown system properties and have to be determined by model-data-fusion at the scale of interest. Furthermore, state estimation methods are nowadays applied to update state variables and minimize model error. Joint state and parameter estimation methods have been developed to combine these two worlds. This session invites contributions on model-data-fusion in the vadose zone and its neighboring compartments with a focus on:
- improved methods to describe model error and prediction uncertainty,
- methods to disentangle parameter, forcing and model error,
- robust estimation techniques capable of handling non-Gaussian and correlated errors,
- assimilation of data from various sources,
- multi-model ensembles to improve uncertainty quantification,
- optimization of experiments and measurement campaigns in the lab and in the field to maximize information content and minimize uncertainty,
- model adequacy testing and model validation.
We are looking forward to receiving contributions which cover the whole range of scales: laboratory, field, pedon, and watershed scale.