Predictions of physical processes in aquifers, rivers and across compartments are strongly affected by uncertainties and errors in model structure, parameters and forcing data. Thus, reliable predictions at any scale (lab/field/catchment) require a rigorous and transparent treatment of these uncertainties, from parameter estimation to uncertainty quantification and model selection. Acknowledging uncertainties and equifinality as a fundamental part of modelling and understanding model-parameter interactions during calibration opens up otherwise-missed opportunities for scientific insight and decision support. This session is a platform for discussion of methodological advances and workflows addressing inverse problems in surface and subsurface hydrology, i.e., using available observed data to gain knowledge/ constrain uncertainty about related but unobserved quantities of interest. We invite contributions on improved concepts, approaches & computational algorithms (be they Bayesian, frequentist, optimization- or ML-based) as well as demonstrations of best practices, challenges & pitfalls, especially (but not exclusively) related to:

- parameter inference, model selection/ averaging, sensitivity and uncertainty analysis;
- representation of uncertain data and boundary conditions;
- integration of heterogeneous/multi-source data;
- identification and treatment of model-structural errors;
- distilling new model formulations (data-driven, physics-based, knowledge-guided or hybrid);
- data worth and optimal experimental design strategies toward maximum information/minimum uncertainty;
- constraint learning/ novel likelihood formulations to incorporate expert knowledge in inversion;
- other regularization strategies that help solve ill-posed problems;
- computational efficiency of solving inverse problems, including surrogate and ML-based techniques;
- Benchmarking and intercomparison efforts on synthetic or real-world, local or large-sample data-sets;
- transparent and reproducible workflows for robust predictions and visualization/communication of inference results to stakeholders;
- real-time inversion for operational forecasting;
- variations of all the above specific to low-dimensional, high-dimensional, dynamic, spatially distributed, geostatistical, linear, or non-linear inverse-problem settings.