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.