The estimation of the spatial and temporal
variability of the hydrologic and energy budgets of the land surface is
a central objective of studies such as the GEWEX Continental Scale International
Project (GCIP), and is crucial for improved climate simulation, numerical
weather prediction and water resources management. Macroscale hydrological
modeling is a powerful tool with which to develop predictive understanding
of the large scale dynamics of these budgets. However, hydrological modeling
at continental scales and beyond is greatly hindered by the scarcity of
land surface observations needed to drive the models.
Remote sensing promises to revolutionize large
scale hydrological modeling by providing an alternative to the use of ground
observations that historically have been the sole model forcings. The challenge
is to design hydrologic model structures and remote sensing methodologies
that make best use of the largely untapped potential of satellite observations
for water resources management during the EOS-era.