Abstract:
The spatial and temporal variability of the terrestrial hydrological cycle 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, despite the fact that a large percentage of total freshwater use is allocated for irrigation (more than 60% in the United States). Satellite observations can monitor the hydrological cycle in its entirety. By combining model estimates and observations, data assimilation allows to estimate hydrological states better than either source of information individually. This study aims 1) to develop and validate the Catchment land surface model of the Goddard Earth Observing System Model (GEOS) by explicitly introducing irrigation schemes, and 2) to assimilate SMAP soil moisture satellite observations to overcome the limited irrigation data in some regions. In the improved model, irrigation can be triggered via two different methodologies to identify crop growing seasons: 1) a global crop calendar for 26 predominant crop types; 2) the Leaf Area Index (LAI) exceeding a prefixed value. Depending on the crop type, both methods allow for three different ways to apply irrigation: 1) sprinkler, 2) drip and 3) flood technology. The scheme updates irrigation rates daily by checking the value of the root zone soil moisture relative to a pre-defined threshold during the growing period. By explicitly accounting for irrigation in the land surface model and assimilating SMAP soil moisture observations, we expect to gain a better understanding of anthropogenic and natural changes to the hydrologic cycle.