Geoscience Reference
In-Depth Information
nonlinear effects of the local human interferences with the hydrological cycle through land-
use changes and do not contain a detailed and realistic representation especially of the
terrestrial hydrological cycle and the strongly nonlinear response mainly of its components
soil and vegetation during times of water shortage. These important research issues can
hardly be addressed by most present hydrological models, which are often conceptual in
nature, calibrated to measured data to various degrees and often data poor in design. This is
the underlying reason why several authors recently suggest developing new hyper-resolu-
tion hydrological land surface models, which can be fully coupled to regional and global
climate models to combine global with regional hydrology. These models should be based
on first-order principles, close the terrestrial energy, water and carbon balance at all scales,
should be able to overcome these scale issues by coupling the very small with the very large
processes and should be able to tap the rich global data archives available through remote
sensing (Hibbard et al.
2010
; Wood et al.
2011
; Mauser and Bach
2009
; Su et al.
2010
).
However, research on these multi-scale issues is currently also severely hampered by
the lack of observations of key variables with adequate resolution related to hydrological
cycle change in space and time. The measurement of precipitation, for instance, either by
ground stations, radars or satellites, is still a challenging task at all spatial and temporal
scales that could hamper efforts devoted to understanding and modelling the hydrological
cycle and its variability. Surface soil moisture is one of the least observed variables and
only recently, with the advent of ESA's Soil Moisture and Ocean Salinity (SMOS) mission,
became accessible to global spatio-temporal measurements with a very coarse spatial but
almost adequate temporal resolution.
Vegetation response to water stress and its effect on water release to the atmosphere has
to be inferred from models, which simulate evapotranspiration and vegetation surface
temperature, which can then be compared to remote sensing measurements under cloud-
free conditions. Moreover, the terrestrial snow and ice masses are important components of
the global climate system. Snow and ice influence the radiation and surface energy budget,
the moisture balance, gas and particle fluxes, precipitation, hydrology, and atmospheric
and oceanic circulation. These processes are coupled with the global climate system
through complex feedbacks that are not yet well understood. Improved observational data
are therefore needed for a better understanding and accurate quantification of the main
cryospheric processes and the corresponding representation of the cryosphere in climate
models (Lemke et al.
2007
).
Remote sensing offers the possibility of delivering the kind of data that allows
observing with adequate resolution how key variables related to hydrological cycle change
in space and time. Most remote sensing measurements indirectly observe the hydrological
cycle and can only be utilized to their full potential when assimilated into appropriate
hydrological models. Some recent hydrological model developments have taken up this
task (Mauser and Bach
2009
) and have shown how to assimilate remote sensing data
streams into hydrological models (Bach
2003
). Nevertheless, hydrological land surface
models face the challenge of a future data-rich environment. They have to learn how to
assimilate on all scales globally available, high spatial resolution, frequent coverage
remote sensing data, e.g., from the operational Sentinel satellites of the European Com-
mission - European Space Agency (EC-ESA) Copernicus program. This is expected to
generate the necessary knowledge to successfully approach issues of the local to global
hydrological cycle as outlined above. Critical elements to accomplish this are related to (1)
the quality of the observing system, jointly combining the ground-based network and the
Earth observation (EO); (2) removal of major knowledge gaps; and (3) development and
implementation of high-quality hydrological models with the ability to assimilate EO data.
