Abstract:
The Earth's land surface is characterized by tremendous natural heterogeneity and human engineered modifications, both of which are significantly challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is one of the important human induced modifications to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this study, irrigation is used as an example of an important human engineered, unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental U.S. is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation artifacts is mixed, with ASCAT based products generally more skillful than SMOS and AMSR2 products. In this study, the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations is also explored. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.