  Prototypes of electromagnetic and hadronic imaging calorimeters developed and
operated by the CALICE collaboration provide an unprecedented wealth of highly
granular data of hadronic showers for a variety of active sensor elements and
different absorber materials. In this presentation, we discuss detailed
measurements of the spatial and the time structure of hadronic showers to
characterise the different stages of hadronic cascades in the calorimeters,
which are then confronted with GEANT4-based simulations using different
hadronic physics models. These studies also extend to the two different
absorber materials, steel and tungsten, used in the prototypes. The high
granularity of the detectors is exploited in the reconstruction of hadronic
energy, both in individual detectors and combined electromagnetic and hadronic
systems, making use of software compensation and semi-digital energy
reconstruction. The results include new simulation studies that predict the
reliable operation of granular calorimeters. Further we show how granularity
and the application of multivariate analysis algorithms enable the separation
of close-by particles. We will report on the performance of these
reconstruction techniques for different electromagnetic and hadronic
calorimeters, with silicon, scintillator and gaseous active elements. Granular
calorimeters are also an ideal testing ground for the application of machine
learning techniques. We will outline how these techniques are applied to CALICE
data and in the CALICE simulation framework.
