Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model

Irrigation representation in land surface models has been advanced over the past decade, but the soil moisture (SM) data from SMAP satellite have not yet been utilized in large-scale irrigation modeling. 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. Data assimilation also enhances SM simulations in CLM4.5. These results could provide a basis for improved modeling of irrigation and land-atmosphere interactions.