Utilizing SMAP soil moisture data to constrain irrigation in the Community Land Model

Here we investigate the potential of improving irrigation representation in the Community Land Model version-4.5 (CLM4.5) by assimilating SMAP data. Simulations are conducted over the heavily irrigated central U.S. region.

We find that constraining the target SM in CLM4.5 using SMAP data assimilation with 1-D Kalman filter reduces the root-mean-square error of simulated irrigation water requirement by 50% on average (for Nebraska, Kansas, and Texas) and significantly improves irrigation simulations by reducing the bias in irrigation water requirement by up to 60%. An a priori bias correction of SMAP data further improves these results in some regions but incrementally.