  The near-surface environment is often too complex to enable inference of
hydrological and environmental variables using one geophysical data type alone.
Joint inversion and coupled inverse modeling involving numerical flow- and
transport simulators have, in the last decade, played important roles in
pushing applications towards increasingly challenging targets. Joint inversion
of geophysical data that is based on structural constraints is often favored
over model coupling based on explicit petrophysical relationships. More
specifically, cross-gradient joint inversion has been applied to a wide range
of near-surface applications and geophysical data types. To infer hydrological
subsurface properties, the most appropriate approach is often to use temporal
changes in geophysical data that can be related to hydrological state
variables. This allows using geophysical data as indirect hydrological
observables, while the coupling with a flow- and transport simulator ensures
physical consistency. Future research avenues include investigating the
validity of different coupling strategies at various scales, the spatial
statistics of near-surface petrophysical relationships, the influence of the
model conceptualization, fully probabilistic joint inversions, and how to
include complex prior information in the joint inversion.
