Abstract:
The spatial and temporal variability of terrestrial hydrology
is driven by both natural and anthropogenic processes. While
natural processes, such as precipitation-induced runoff or
evaporation, are included in most global land surface models,
anthropogenic processes, such as irrigation, are rarely modeled.
Satellite observations are one of the great sources of
information to monitor the hydrological cycle in its entirety.
Data assimilation, the weighted combination of model predictions
and observations, allows to estimate hydrological states
better than either source of information individually. The
central hypothesis of this project is that a better understanding
of anthropogenic and natural changes to the hydrologic
cycle can be achieved by using multi-variate assimilation and
land surface models with improved natural and anthropogenic
process descriptions. Generally, data assimilation algorithms
used for land surface data assimilation operate best when the
modeling system is unbiased with respect to the assimilated
observations. To achieve unbiased hydrological estimates, we
introduce an irrigation module in the NASA Goddard Earth
Observing System (GEOS). This presentation reports the key
features and results of our work.