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HS8.1.3

Model Uncertainties, Parameter Estimation, and Data Assimilation in Subsurface Hydrology
Convener: Wolfgang Nowak  | Co-Conveners: Harrie-Jan Hendricks Franssen , Thomas Wöhling , Olaf Ippisch , Monica Riva , Alberto Guadagnini , Xavier Sanchez-Vila , Daniel Fernandez-Garcia 
Orals
 / Fri, 22 Apr, 08:30–10:00  / Room 2.95
Posters
 / Attendance Fri, 22 Apr, 13:30–15:00  / Hall A
Predictions of chemical, biological and physical processes in soils, aquifers and across compartments (e.g., stream-aquifer interactions) are strongly affected by errors in model structure, parameters, and forcing data. The determination of optimal parameter sets and the quantification of uncertainty are therefore crucial for reliable predictions. This session invites contributions that focus on improved measurement, parameter estimation and uncertainty quantification techniques on all scales (lab/field/catchment), especially studies on:
- new measurement technologies that either can aid in the parameterization of models for subsurface flow and transport, chemical reactions and (micro)biological processes, or can help to diagnose and detect structural model deficiencies.
- parameter estimation schemes which provide a synthesis of spatially distributed parameter fields and prior information of the underlying geology.
- optimal experimental design strategies that maximize information retrieval from measurements on subsurface systems and minimize prediction uncertainty.
- the contribution of different observation types (from point scale physics measurements to remote sensing data and “soft information”) to uncertainty reduction.
In the past years much progress has been made in the area of parameter uncertainty estimation, but much less attention has been paid to representation of model and forcing data errors. We therefore especially encourage submissions on the following topics:
- novel theories and concepts for spatial and temporal analysis of the model - data mismatch. This ranges from pure optimization based methods to sequential data assimilation.
- formal and informal statistical frameworks that diagnose, detect and resolve all sources of modelling errors.
- approaches (e.g. multi-model ensemble systems) that capture conceptual model uncertainty.
- virtual soil systems as a tool to assess the effect of heterogeneity and hydraulic parameter functions on the effective behavior of soils.