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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.

Plain Language Summary About 90% of consumptive water use is allocated for irrigation sector globally. Irrigation not only affects water resources and hydrology but also alters local to regional climate and weather systems. Therefore, it is important to represent irrigation in land and climate models for better assessment and prediction of water resources and to understand the potential feedback to climate system. Given the lack of global irrigation data, irrigation schemes currently included in large-scale land surface models often use simplified representation to simulate irrigation. The goal of this study is to improve the estimation of irrigation water in these global models by using the newly available soil moisture information from the recent National Aeronautics and Space Administration (NASA) satellite, the Soil Moisture Active Passive (SMAP). We use the widely used Community Land Model and apply it over the heavily irrigated areas in the central United States. Our results indicate that SMAP data assimilation yields a significant improvement in the simulation of irrigation water use. These results have important implications for better assessment and prediction of irrigation water use and for studying human-climate interactions.