There has been a growing awareness among hydrologists and water resources analysts of the need to quantify the uncertainty associated with outcomes produced by models. A similar awareness is growing among regulatory agencies, decision makers, stakeholders and informed segments of the general public. Sources of uncertainty recognized as being important include the underlying conceptual framework and its mathematical-numerical representation, measurement and interpretive data errors and support scales, natural variability and scale dependence, stochasticity of forcing terms, and difficulties in predicting future system operating scenarios. Current ways of assessing these uncertainties and their propagation through models include (among others) multimodel analyses within information-theoretic or Bayesian contexts, reliance on geostatistical and stochastic models, sensitivity analysis, and probabilistic risk assessment. Reduction of uncertainty is accomplished through data assimilation techniques including parameter estimation and optimal design of experimental, observational, and monitoring systems. This session is focused on the quantification and reduction of uncertainties impacting hydrologic models of processes occurring wholly or in part in the subsurface. We invite colleagues to present new ideas and methods concerning (1) sources of uncertainty impacting subsurface flow and transport models, (2) uncertainty quantification and reduction, (3) impact of uncertainty on our understanding of hydrogeologic processes, (4) communication of uncertainty related issues to decision makers and the public, (5) evaluation and comparison of available uncertainty assessment/reduction tools and techniques, (6) introduction of recent software and user interfaces, and (7) design of optimal data collection schemes in the face of uncertainty. Theoretical and numerical developments and case studies of these and related issues will be considered for oral and poster presentation.